U.S. patent application number 14/355385 was filed with the patent office on 2014-10-09 for gene signature for the prediction of nf-kappab activity.
This patent application is currently assigned to H. Lee Moffitt Cancer and Research Institute, Inc.. The applicant listed for this patent is H. Lee Moffitt Cancer Center and Research Institute, Inc.. Invention is credited to Amer A. Beg, Dung-Tsa Chen, Steven A. Enkemann.
Application Number | 20140302060 14/355385 |
Document ID | / |
Family ID | 48192768 |
Filed Date | 2014-10-09 |
United States Patent
Application |
20140302060 |
Kind Code |
A1 |
Beg; Amer A. ; et
al. |
October 9, 2014 |
Gene Signature for the Prediction of NF-kappaB Activity
Abstract
Methods for predicting NF-kappaB (NF-kB) activity in a tumor,
and more particularly to methods for predicting survival and
therapeutic outcome, and selecting therapy in subjects with tumors,
e.g., adenocarcinomas, e.g., lung adenocarcinomas and
melanomas.
Inventors: |
Beg; Amer A.; (Tampa,
FL) ; Enkemann; Steven A.; (Lutz, FL) ; Chen;
Dung-Tsa; (Tampa, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
H. Lee Moffitt Cancer Center and Research Institute, Inc. |
Tampa |
FL |
US |
|
|
Assignee: |
H. Lee Moffitt Cancer and Research
Institute, Inc.
Tampa
FL
|
Family ID: |
48192768 |
Appl. No.: |
14/355385 |
Filed: |
November 1, 2012 |
PCT Filed: |
November 1, 2012 |
PCT NO: |
PCT/US2012/063087 |
371 Date: |
April 30, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61554314 |
Nov 1, 2011 |
|
|
|
Current U.S.
Class: |
424/158.1 ;
506/9 |
Current CPC
Class: |
C12Q 2600/158 20130101;
A61K 31/4965 20130101; A61K 31/353 20130101; A61K 31/4468 20130101;
C12Q 1/6886 20130101; C12Q 2600/106 20130101; A61K 35/17 20130101;
A61K 35/30 20130101; A61K 38/2013 20130101; A61K 31/436 20130101;
A61K 31/05 20130101; A61P 35/00 20180101; C12Q 2600/118 20130101;
A61K 38/06 20130101; C12Q 1/6813 20130101 |
Class at
Publication: |
424/158.1 ;
506/9 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Goverment Interests
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] This invention was made with Government support under Grant
Nos. W81XWH-08-2-0101 awarded by the Department of Defense
(ARMY/MRMC) and SPORE NCI P50 CA119997 awarded by the National
Institutes of Health. The Government has certain rights in the
invention.
Claims
1. A method of predicting NF-kB transcriptional activity in a
tumor, the method comprising: determining gene expression levels
for guanylate binding protein 1, interferon-inducible (GBP1);
proteasome (prosome, macropain) subunit, beta type, 9 (large
multifunctional peptidase 2) (PSMB9); interferon regulatory factor
1 (IRF1); transporter 1, ATP-binding cassette, sub-family B
(MDR/TAP) (TAP1); tumor necrosis factor, alpha-induced protein 3
(TNFAIP3); chemokine (C-C motif) ligand 5 (CCL5); proteasome
(prosome, macropain) subunit, beta type, 8 (large multifunctional
peptidase 7) (PSMB8); interleukin 32 (IL32); SH2B adaptor protein 3
(SH2B3); and nuclear factor of kappa light polypeptide gene
enhancer in B-cells inhibitor, epsilon (NFKBIE) in a sample
comprising cells from the tumor; and comparing the gene expression
levels to reference levels; optionally assigning a score to the
tumor based on the comparison of the gene expression levels in the
tumor to the reference levels; wherein the presence of gene
expression levels above the reference levels, or a score above a
threshold score, indicates that the tumor has high levels of NF-kB
activity, and the presence of gene expression levels below the
reference levels, or a score below the threshold score indicates
that the tumor has low levels of NF-kB activity.
2. The method of claim 1, further comprising identifying a subject
as having a tumor having high levels of NF-kB activity, and
selecting a treatment for the subject comprising administering an
NF-kB inhibitor or immunotherapy.
3. The method of claim 1, further comprising identifying a subject
as having a tumor having low levels of NF-kB activity, and
selecting a treatment for the subject comprising administering an
NF-kB activator and immunotherapy.
4. A method of selecting a treatment for a subject who has a tumor,
the method comprising: determining gene expression levels for GBP1,
PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and NFKBIE in
a sample comprising cells from the tumor; and comparing the gene
expression levels to reference levels; optionally assigning a score
to the tumor based on the comparison of the gene expression levels
in the tumor to the reference levels; detecting the presence of
gene expression levels above the reference levels, or a score above
a threshold score, and selecting a treatment for the subject
comprising administering an NF-kB inhibitor or immunotherapy; or
detecting the presence of gene expression levels below the
reference levels, or a score below a threshold score, and selecting
a treatment for the subject comprising administering an NF-kB
activator and immunotherapy.
5. The method of claim 4, further comprising administering the
selected treatment to the subject.
6. A method of predicting outcome in a subject with a tumor, the
method comprising: determining gene expression levels for GBP1,
PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and NFKBIE in
a sample comprising cells from the tumor; and comparing the gene
expression levels to reference levels; optionally assigning a score
to the tumor based on the comparison of the gene expression levels
in the tumor to the reference levels; wherein the presence of gene
expression levels below the reference levels, or a score below a
threshold score, indicate that the subject is likely to have a poor
outcome as compared to a subject who has levels above the reference
levels or threshold score, and the presence of gene expression
levels above the reference levels, or a score above a threshold
score, indicate that the subject is likely to have a better outcome
as compared to a subject who has levels below the reference levels
or threshold score.
7. (canceled)
8. (canceled)
9. (canceled)
10. The method of any of claim 1, wherein the tumor is an
adenocarcinoma.
11. The method of claim 10, wherein the adenocarcinoma is lung
adenocarcinoma or melanoma.
12. The method of claim 1, wherein determining gene expression
levels comprises performing an assay to determine gene expression
levels in the sample.
13. The method of claim 1, wherein assigning a score to the tumor
based on the comparison of the gene expression levels in the tumor
to the reference levels comprises using an algorithm to calculate a
score.
14. The method of claim 4, wherein the NF-kB activator is an
anticancer agent, preferably selected from the group consisting of
taxanes, vinca alkaloids, and topoisomerase inhibitors.
15. The method of claim 4, wherein the NF-kB inhibitor is selected
from the group consisting of sulfasalazine, Luteolin, rapamycin,
temsirolimus and everolimus, caffeic acid phenethylester, SN50,
parthenolide, triptolide, wedelolactone, lactacystin, substituted
resorcinols, (E)-3-(4-methylphenylsulfonyl)-2-propenenitrile, Bay
11-7082, Bay 11-7821, or Bay 11-7085, Pranlukast, etoposide,
bortezomib, MLN9708, PS-1145, tetrahydrocurcuminoids, such as
Tetrahydrocurcuminoid CG, extracts of Paulownia tomentosa wood, and
MG-132 (Z-Leu-Leu-Leu-H).
16. The method of claim 4, wherein the immunotherapy is selected
from the group consisting of administration of dendritic cells or
peptides with adjuvant; DNA-based vaccines; cytokines;
cyclophosphamide; anti-interleukin-2R immunotoxins; and antibodies,
virus-based vaccines, formulations of Toll-like Receptor or
RIG-I-like receptor ligands, Adoptive T cell therapy or other cell
types.
17. The method of claim 16, wherein the antibodies are selected
from the group consisting of anti-CD137, anti-PD1, anti-CD40,
anti-PDL1, and anti-CTLA-4 antibodies.
Description
CLAIM OF PRIORITY
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 61/554,314, filed Nov. 1, 2011, the entire
contents of which are incorporated by reference herein.
TECHNICAL FIELD
[0003] This invention relates to methods for predicting NF-kappaB
(NF-kB) activity in a tumor, and more particularly to genes and
gene signatures that predict survival and therapeutic outcome in
subjects with tumors, e.g., adenocarcinoma, e.g., lung
adenocarcinoma or melanoma.
BACKGROUND
[0004] The NF-kB family of transcription factors plays a crucial
role in many cellular responses. They exist as homodimers or
heterodimers of 5 distinct proteins: p50, p52, p65/RelA, RelB and
cRel (Li and Verma, 2002. Nat Rev Immunol 2:725-734; Hayden and
Ghosh, 2004. Genes Dev 18:2195-2224). NF-kB activation typically
occurs by nuclear translocation following inducible phosphorylation
of inhibitory IkB proteins by the IKKa/b (IkB kinase) complex
(Hayden and Ghosh, 2004. Genes Dev 18:2195-2224; Hoffmann, and
Baltimore, 2006. Immunol Rev 210:171-186; Karin and Ben-Neriah,
2000. Annu Rev Immunol 18:621-663; Vallabhapurapu and Karin, 2009.
Annu Rev Immunol 27:693-733). Activation of the major conventional
or canonical subunits p50, p65/RelA and cRel by inflammatory
cytokines such as TNFa and IL-1a/b requires IKKb while
non-conventional or non-canonical p52 and RelB subunits require
IKKa (Bonizzi and Karin, 2004. Trends Immunol 25:280-288; Hacker
and Karin, 2006. Sci STKE 2006:re13). A multitude of functions have
been attributed to canonical NF-kB subunits, which include roles in
inflammation and immunity, as well as in cell proliferation and
survival. A tumor-promoting function for NF-kB in lymphomas has
been known for some time (Staudt, 2010. Cold Spring Harb Perspect
Biol 2:a000109; Packham, 2008. Br J Haematol 143:3-15). NF-kB was
implicated in the Ras pathway (Mayo et al., 1997. Science
278:1812-1815) but the role of NF-kB in solid malignancies,
although suspected, was not clear. Recent studies in mice and human
cell lines have defined a key role for NF-kB in K-Ras-induced lung
cancer ((Barbie et al., 2009. Nature 462:108-112; Meylan et al.,
2009. Nature 462:104-107; Basseres et al., 2010. Cancer Res
70:3537-3546). This is likely through NF-kB activation via IKKb
and/or TBK1 kinase by oncogenic K-Ras (Barbie et al., 2009. Nature
462:108-112; Meylan et al., 2009. Nature 462:104-107; Basseres et
al., 2010. Cancer Res 70:3537-3546). It is not known whether NF-kB
plays a general or genetic mutation-specific role in lung cancer
development. An additional key role of IKKb/NF-kB in
inflammation-promoting non-tumor myeloid cell types has also been
shown to be critical in solid malignancies (Maeda et al., 2005.
Cell 121:977-990; Karin and Greten, 2005. Nat Rev Immunol
5:749-759; Greten et al., 2004. Cell 118:285-296; Takahashi et al.,
2010. Cancer Cell 17:89-97).
[0005] Inhibition of NF-kB activation represents a promising avenue
for therapeutic targeting of lung cancer. However, relatively
little is known about the role and activation state of NF-kB in
human lung cancer. Most importantly, the inter-relation between
NF-kB activation state and disease progression and survival is not
known. It is also not known whether NF-kB is only activated in
response to specific genetic mutations, e.g., K-Ras mutations. In
this respect, one of the main stumbling blocks is the lack of an
appropriate functional readout of NF-kB activation in human lung
cancer cells. Previous NF-kB signatures have been defined, but not
in lung cancer cells (Hinata et al., 2003. Oncogene 22:1955-1964;
Boehm et al., 2007. Cell 129:1065-1079; Hernandez et al., 2010.
Cancer Res 70:4005-4014).
SUMMARY
[0006] The present invention is based, at least in part, on the
discovery of a set of genes that can be used to predict NF-kB
activity, survival, and outcome in cancers, e.g., carcinoma, e.g.,
adenocarcinoma, e.g., human lung adenocarcinoma or melanoma.
[0007] Thus, in a first aspect, the invention provides methods for
predicting NF-kB transcriptional activity in a tumor. The methods
include determining gene expression levels for guanylate binding
protein 1, interferon-inducible (GBP1); proteasome (prosome,
macropain) subunit, beta type, 9 (large multifunctional peptidase
2) (PSMB9); interferon regulatory factor 1 (IRF1); transporter 1,
ATP-binding cassette, sub-family B (MDR/TAP) (TAP 1); tumor
necrosis factor, alpha-induced protein 3 (TNFAIP3); chemokine (C-C
motif) ligand 5 (CCL5); proteasome (prosome, macropain) subunit,
beta type, 8 (large multifunctional peptidase 7) (PSMB8);
interleukin 32 (IL32); SH2B adaptor protein 3 (SH2B3); and nuclear
factor of kappa light polypeptide gene enhancer in B-cells
inhibitor, epsilon (NFKBIE) in a sample comprising cells from the
tumor; and
comparing the gene expression levels to reference levels;
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels;
wherein the presence of gene expression levels above the reference
levels, or a score above a threshold score, indicates that the
tumor has high levels of NF-kB activity, and the presence of gene
expression levels below the reference levels, or a score below the
threshold score indicates that the tumor has low levels of NF-kB
activity.
[0008] In some embodiments, the methods include identifying a
subject as having a tumor having high levels of NF-kB activity, and
selecting a treatment for the subject comprising administering an
NF-kB inhibitor or immunotherapy.
[0009] In some embodiments, the methods include identifying a
subject as having a tumor having low levels of NF-kB activity, and
selecting a treatment for the subject comprising administering an
NF-kB activator and immunotherapy.
[0010] In another aspect, the invention provides methods for
selecting a treatment for a subject who has a tumor. The methods
include determining gene expression levels for GBP1, PSMB9, IRF1,
TAP1, TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and NFKBIE in a sample
comprising cells from the tumor; and
comparing the gene expression levels to reference levels;
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels;
detecting the presence of gene expression levels above the
reference levels, or a score above a threshold score, and selecting
a treatment for the subject comprising administering an NF-kB
inhibitor or immunotherapy; or detecting the presence of gene
expression levels below the reference levels, or a score below a
threshold score, and selecting a treatment for the subject
comprising administering an NF-kB activator and immunotherapy.
[0011] In another aspect, the invention provides methods for
treating a subject who has a tumor. The methods include determining
gene expression levels for GBP1, PSMB9, IRF1, TAP1, TNFAIP3, CCL5,
PSMB8, IL32, SH2B3 and NFKBIE in a sample comprising cells from the
tumor; and
comparing the gene expression levels to reference levels;
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels;
and detecting the presence in the subject of gene expression levels
above the reference levels, or a score above a threshold score, and
administering a treatment to the subject comprising an NF-kB
inhibitor or immunotherapy, or detecting the presence in the
subject of gene expression levels below the reference levels, or a
score below a threshold score, and administering a treatment to the
subject comprising an NF-kB activator and immunotherapy.
[0012] In another aspect, the invention provides methods for
predicting outcome in a subject with a tumor. The methods include
determining gene expression levels for GBP1, PSMB9, IRF1, TAP1,
TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and NFKBIE
in a sample comprising cells from the tumor; and comparing the gene
expression levels to reference levels; optionally assigning a score
to the tumor based on the comparison of the gene expression levels
in the tumor to the reference levels; wherein the presence of gene
expression levels below the reference levels, or a score below a
threshold score, indicate that the subject is likely to have a poor
outcome as compared to a subject who has levels above the reference
levels or threshold score, and the presence of gene expression
levels above the reference levels, or a score above a threshold
score, indicate that the subject is likely to have a better outcome
as compared to a subject who has levels below the reference levels
or threshold score.
[0013] In another aspect, the invention provides methods for
predicting outcome in a subject with a tumor. The methods include
determining gene expression levels for some or all of CXCL1, CXCL2,
CXCL3, IL6, and IL8, e.g., CXCL1, CXCL3, IL6, and IL8, in a sample
comprising cells from the tumor; and
comparing the gene expression levels to reference levels;
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels;
wherein the presence of gene expression levels above the reference
levels, or a score above a threshold score, indicate that the
subject is likely to have a poor outcome as compared to a subject
who has levels below the reference levels or threshold score, and
the presence of gene expression levels below the reference levels,
or a score above a threshold score, indicate that the subject is
likely to have a better outcome as compared to a subject who has
levels below the reference levels or threshold score.
[0014] In another aspect, the invention provides methods for
predicting outcome in a subject with a tumor. The methods include
determining gene expression levels of LTB in a sample comprising
cells from the tumor; and
comparing the gene expression levels to reference levels;
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels;
wherein the presence of gene expression levels below the reference
levels, or a score below a threshold score, indicate that the
subject is likely to have a poor outcome as compared to a subject
who has levels above the reference levels or threshold score, and
the presence of gene expression levels above the reference levels,
or a score above a threshold score, indicate that the subject is
likely to have a better outcome as compared to a subject who has
levels below the reference levels or threshold score.
[0015] In another aspect, the invention provides methods for
monitoring treatment in a subject who has a tumor with low NF-kB
activity. The methods include determining gene expression levels
for GBP1, PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and
NFKBIE in a first sample comprising cells from the tumor; and
comparing the gene expression levels in the first sample to
reference levels; optionally assigning a score to the tumor based
on the comparison of the gene expression levels in the first sample
to the reference levels; identifying a subject as having a tumor
having low levels of NF-kB activity based on the presence of gene
expression levels below the reference levels, or a score below the
threshold score; administering to the subject a treatment,
preferably a treatment comprising administering one or both of an
NF-kB activator and immunotherapy; determining gene expression
levels for GBP1, PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8, IL32,
SH2B3 and NFKBIE in a subsequent sample comprising cells from the
tumor; and comparing the gene expression levels in the subsequent
sample to levels in the first sample, wherein an increase in the
levels from the first sample to the subsequent sample indicates
that the treatment has been effective, and no change or a decrease
in the levels from the first sample to the subsequent samples
indicates that the treatment has not been effective.
[0016] In some embodiments, the tumor is a carcinoma, e.g.,
adenocarcinoma, e.g., lung adenocarcinoma or melanoma.
[0017] In some embodiments, determining gene expression levels
comprises performing an assay to determine gene expression levels
in the sample.
[0018] In some embodiments, assigning a score to the tumor based on
the comparison of the gene expression levels in the tumor to the
reference levels comprises using an algorithm to calculate a
score.
[0019] In some embodiments, the NF-kB activator is an anticancer
agent, preferably selected from the group consisting of taxanes,
vinca alkaloids, and topoisomerase inhibitors.
[0020] In some embodiments, the NF-kB inhibitor is selected from
the group consisting of sulfasalazine, Luteolin, rapamycin,
temsirolimus and everolimus, caffeic acid phenethylester, SN50,
parthenolide, triptolide, wedelolactone, lactacystin, substituted
resorcinols, (E)-3-(4-methylphenylsulfonyl)-2-propenenitrile, Bay
11-7082, Bay 11-7821, or Bay 11-7085, Pranlukast, etoposide,
bortezomib, MLN9708, PS-1145, tetrahydrocurcuminoids, such as
Tetrahydrocurcuminoid CG, extracts of Paulownia tomentosa wood, and
MG-132 (Z-Leu-Leu-Leu-H).
[0021] In some embodiments, the immunotherapy is selected from the
group consisting of administration of dendritic cells or peptides
with adjuvant; DNA-based vaccines; cytokines (e.g., IL-2);
cyclophosphamide; anti-interleukin-2R immunotoxins; and antibodies,
virus-based vaccines (e.g., adenovirus), formulations of Toll-like
Receptor or RIG-I-like receptor ligands, Adoptive T cell therapy or
other cell types.
[0022] In some embodiments, the antibodies are selected from the
group consisting of anti-CD137, anti-PD1, anti-CD40, anti-PDL1, and
anti-CTLA-4 antibodies.
[0023] In another aspect, the invention provides methods for
predicting NF-kB transcriptional activity in a tumor. The methods
include determining gene expression levels for some or all of the
genes shown herein, e.g., in Tables A or 1 or 3, or the ten genes
GBP1, PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and
NFKBIE, in a sample comprising cells from the tumor; comparing the
gene expression levels to reference levels; and assigning a score
to the tumor based on the comparison of the gene expression levels
in the tumor to the reference levels. A score above a threshold
score indicates that the tumor has high levels of NF-kB activity
(and conversely, a score below the threshold indicates low NF-kB
activity).
[0024] In some embodiments, the methods include identifying a
subject as having a tumor having high levels of NF-kB activity, and
selecting a treatment for the subject comprising administering an
NF-kB inhibitor or immunotherapy.
[0025] In a further aspect, the invention provides methods of
predicting outcome in a subject with cancer. The methods include
determining gene expression levels for some or all of CXCL1, CXCL2,
CXCL3, and IL8 in a sample comprising cells from the tumor;
comparing the gene expression levels to reference levels; and
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels.
The presence of gene expression levels above the reference levels
indicate that the subject is likely to have a poor outcome (and
conversely, levels below the reference levels indicate a good
outcome).
[0026] In some embodiments, the methods include identifying a
subject as having a tumor having high gene expression levels, and
selecting a treatment for the subject comprising administering an
NF-kB inhibitor or immunotherapy.
[0027] In yet another aspect, the invention features methods for
predicting outcome in a subject with cancer, The methods include
determining gene expression levels of LTB in a sample comprising
cells from the tumor; comparing the gene expression levels to
reference levels; and optionally assigning a score to the tumor
based on the comparison of the gene expression levels in the tumor
to the reference levels. The presence of gene expression levels
above the reference levels indicate that the subject is likely to
have an improved outcome.
[0028] In some embodiments, the methods include identifying a
subject as having a tumor having high LTB gene expression levels,
and selecting a treatment for the subject comprising administering
an NF-kB inhibitor or immunotherapy.
[0029] In another aspect, the invention features methods for
predicting outcome in a subject with cancer. The methods include
determining gene expression levels for some or all of CCL2, CCL5,
LTB, CD83, and RELB in a sample comprising cells from the tumor;
and comparing the gene expression levels to reference levels;
optionally assigning a score to the tumor based on the comparison
of the gene expression levels in the tumor to the reference levels;
wherein the presence of gene expression levels above the reference
levels indicate that the subject is likely to have an improved
outcome.
[0030] In some embodiments, the methods include identifying a
subject as having a tumor having high gene expression levels, and
selecting a treatment for the subject comprising administering an
NF-kB inhibitor or immunotherapy.
[0031] In some embodiments of the methods described herein, the
tumor is a lung adenocarcinoma.
TABLE-US-00001 TABLE A GenBank ID GeneSymbol Gene description
NM_000214.1 JAG1 jagged 1 (Alagille syndrome) NM_001166.3 BIRC2
baculoviral IAP repeat-containing 2 NM_001165 BIRC3 baculoviral IAP
repeat-containing 3 NM_005504.4 BCAT1 branched chain
aminotransferase 1, cytosolic NM_001710.4 BF B-factor, properdin
NM_000064.1 C3 complement component 3 NM_001025079.1 CD47 CD47
antigen (Rh-related antigen, integrin- associated signal
transducer) NM_004079.3 CTSS cathepsin S NM_015247 CYLD
cylindromatosis (turban tumor syndrome) NM_001924.2 GADD45A growth
arrest and DNA-damage-inducible, alpha NM_005238.2 ETS1 v-ets
erythroblastosis virus E26 oncogene homolog 1 (avian) NM_002053.1
GBP1 guanylate binding protein 1, interferon- inducible, 67 kDa
NM_001024070.1 GCH1 GTP cyclohydrolase 1 (dopa-responsive dystonia)
NM_004004.3 GJB2 gap junction protein, beta 2, 26 kDa (connexin 26)
NM_001511.1 CXCL1 chemokine (C-X-C motif) ligand 1 (melanoma growth
stimulating activity, alpha) NM_002089.1 CXCL2 chemokine (C-X-C
motif) ligand 2 NM_002090.2 CXCL3 chemokine (C-X-C motif) ligand 3
NM_005514.5 HLA-B major histocompatibility complex, class I, B
AY732487.1 HLA-C major histocompatibility complex, class I, C
NM_000201.1 ICAM1 intercellular adhesion molecule 1 (CD54), human
rhinovirus receptor NM_000600.1 IL6 interleukin 6 (interferon, beta
2) NM_002184 IL6ST interleukin 6 signal transducer (gp130,
oncostatin M receptor) NM_000584.2 IL8 interleukin 8 NM_001561.4
TNFRSF9 tumor necrosis factor receptor superfamily, member 9
NM_002192.2 INHBA inhibin, beta A (activin A, activin AB alpha
polypeptide) NM_001570.3 IRAK2 interleukin-1 receptor-associated
kinase 2 NM_002198.1 IRF1 interferon regulatory factor 1
NM_002203.2 ITGA2 integrin, alpha 2 (CD49B, alpha 2 subunit of
VLA-2 receptor) NM_002205.2 ITGA5 integrin, alpha 5 (fibronectin
receptor, alpha polypeptide) NM_000632.3 ITGAM integrin, alpha M
(complement component receptor 3, alpha; also known as CD11b
(p170), macrophage antigen alpha polypeptide) AU144005 ITGAV
integrin, alpha V (vitronectin receptor, alpha polypeptide, antigen
CD51) NM_005562.1 LAMC2 laminin, gamma 2 NM_009588.1 LTB
lymphotoxin beta (TNF superfamily, member 3) NM_004994.2 MMP9
matrix metallopeptidase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa
type IV collagenase) NM_003998.2 NFKB1 nuclear factor of kappa
light polypeptide gene enhancer in B-cells 1 (p105) NM_002502.2
NFKB2 nuclear factor of kappa light polypeptide gene enhancer in
B-cells 2 (p49/p100) NM_004556.2 NFKBIE nuclear factor of kappa
light polypeptide gene enhancer in B-cells inhibitor, epsilon
NM_002526 NT5E 5'-nucleotidase, ecto (CD73) NM_002581.3 PAPPA
pregnancy-associated plasma protein A, pappalysin 1 NM_198833.1
SERPINB8 serpin peptidase inhibitor, clade B (ovalbumin), member 8
NM_001005376.1 PLAUR plasminogen activator, urokinase receptor
NM_004159.4 PSMB8 proteasome (prosome, macropain) subunit, beta
type, 8 (large multifunctional peptidase 7) NM_002800.4 PSMB9
proteasome (prosome, macropain) subunit, beta type, 9 (large
multifunctional peptidase 2) NM_002818.2 PSME2 proteasome (prosome,
macropain) activator subunit 2 (PA28 beta) NM_002852.2 PTX3
pentraxin-related gene, rapidly induced by IL- 1 beta NM_006509.2
RELB v-rel reticuloendotheliosis viral oncogene homolog B, nuclear
factor of kappa light polypeptide gene enhancer in B-cells 3
(avian) NM_002941.2 ROBO1 roundabout, axon guidance receptor,
homolog 1 (Drosophila) NM_002982.3 CCL2 chemokine (C-C motif)
ligand 2 NM_002985.2 CCL5 chemokine (C-C motif) ligand 5
NM_004591.1 CCL20 chemokine (C-C motif) ligand 20 NM_000593.5 TAP1
transporter 1, ATP-binding cassette, sub- family B (MDR/TAP)
NM_000544.3 TAP2 transporter 2, ATP-binding cassette, sub- family B
(MDR/TAP) NM_003190.3 TAPBP TAP binding protein (tapasin)
NM_003264.3 TLR2 toll-like receptor 2 NM_006290.2 TNFAIP3 tumor
necrosis factor, alpha-induced protein 3 NM_005658.3 TRAF1 TNF
receptor-associated factor 1 NM_003300.2 TRAF3 TNF
receptor-associated factor 3 NM_003916 AP1S2 adaptor-related
protein complex 1, sigma 2 subunit NM_021101.3 CLDN1 claudin 1
NM_001012635.1 IL32 interleukin 32 NM_001040280.1 CD83 CD83 antigen
(activated B lymphocytes, immunoglobulin superfamily) NM_004235.3
KLF4 Kruppel-like factor 4 (gut) NM_206853.1 QKI quaking homolog,
KH domain RNA binding (mouse) NM_004289 NFE2L3 nuclear factor
(erythroid-derived 2)-like 3 NM_014840.2 NUAK1 NUAK family,
SNF1-like kinase, 1 NM_005110.1 GFPT2
glutamine-fructose-6-phosphate transaminase 2 NM_005124 NUP153
nucleoporin 153 kDa NM_005475.1 SH2B3 SH2B adaptor protein 3
NM_005493 RANBP9 RAN binding protein 9 NM_005729.3 PPIF
peptidylprolyl isomerase F (cyclophilin F) NM_001008211.1 OPTN
optineurin NM_006058.2 TNIP1 TNFAIP3 interacting protein 1
NM_006470.3 TRIM16 tripartite motif-containing 16 NM_006662 SRCAP
Snf2-related CBP activator protein NM_017585.2 SLC2A6 solute
carrier family 2 (facilitated glucose transporter), member 6
BC067106.1 GPR176 G protein-coupled receptor 176 NM_016445.1 PLEK2
pleckstrin 2 NM_015714.2 G0S2 G0/G1 switch 2 NM_016584.2 IL23A
interleukin 23, alpha subunit p19 NM_001040458.1 ARTS-1 type 1
tumor necrosis factor receptor shedding aminopeptidase regulator
NM_178031.2 HSPA5BP1 heat shock 70 kDa protein 5 (glucose-regulated
protein, 78 kDa) binding protein 1 NM_018370.1 DRAM
damage-regulated autophagy modulator NM_018351.2 FGD6 FYVE, RhoGEF
and PH domain containing 6 NM_020183.3 ARNTL2 aryl hydrocarbon
receptor nuclear translocator- like 2 XM_934503.1 FAM91A2 family
with sequence similarity 91, member A2 NM_022154.5 SLC39A8 solute
carrier family 39 (zinc transporter), member 8 NM_022168.2 IFIH1
interferon induced with helicase C domain 1 NM_022350.1 ERAP2
endoplasmic reticulum aminopeptidase 2 NM_022750.2 PARP12 poly
(ADP-ribose) polymerase family, member 12 NM_022763.2 FNDC3B
Caution, check this probeset carefully. This probeset may detect an
alternate exon, an alternate termination site, or an overlapping
transcript of fibronectin type III domain containing 3B NM_024615.2
PARP8 poly (ADP-ribose) polymerase family, member 8 NM_030952.1
NUAK2 NUAK family, SNF1-like kinase, 2 NM_031449.3 ZMIZ2 zinc
finger, MIZ-type containing 2 NM_032413.2 C15orf48 chromosome 15
open reading frame 48 NM_014903.3 NAV3 neuron navigator 3 NM_138397
LOC93082 hypothetical protein BC012317 NM_030968.2 C1QTNF1 C1q and
tumor necrosis factor related protein 1 NM_173490 LOC134285
hypothetical protein LOC134285 NM_001031739.1 ASB9 ankyrin repeat
and SOCS box-containing 9 NM_178496.2 C3orf59 chromosome 3 open
reading frame 59 NM_144975 MGC19764 hypothetical protein MGC19764
NM_173545.1 C2orf13 chromosome 2 open reading frame 13 NM_147156.3
TMEM23 transmembrane protein 23 NM_207376.1 OCC-1 overexpressed in
colon carcinoma-1 XM_498811.2 KIAA0493 KIAA0493 protein XM_930678.1
LOC642441 hypothetical LOC642441 XM_937100.1 LOC728285 similar to
keratin associated protein 2-4 NM_002223.2 ITPR2 inositol
1,4,5-triphosphate receptor, type 2 NM_002192.2 INHBA Caution, this
probeset may detect an extended transcript or alternative terminal
exon for inhibin, beta A (activin A, activin AB alpha polypeptide)
AK000776 ROR1 Caution, this probeset may detect an extended
transcript or alternative terminal exon for receptor tyrosine
kinase-like orphan receptor 1 AU144005 ITGAV Caution, this probeset
may detect an alternative exon for integrin, alpha V (vitronectin
receptor, alpha polypeptide, antigen CD51) NM_000382.2 ALDH3A2
aldehyde dehydrogenase 3 family, member A2 NM_020987.2 ANK3 ankyrin
3, node of Ranvier (ankyrin G) NM_004058.2 CAPS calcyphosine
NM_001752.2 CAT catalase NM_001875.2 CPS1 carbamoyl-phosphate
synthetase 1, mitochondrial NM_004390.2 CTSH cathepsin H
NM_001352.2 DBP D site of albumin promoter (albumin D-box) binding
protein NM_001005336.1 DNM1 dynamin 1 NM_004409.2 DMPK dystrophia
myotonica-protein kinase NM_001958.2 EEF1A2 eukaryotic translation
elongation factor 1 alpha 2 NM_001408.1 CELSR2 cadherin, EGF LAG
seven-pass G-type receptor 2 (flamingo homolog, Drosophila)
NM_177996.1 EPB41L1 erythrocyte membrane protein band 4.1-like 1
NM_005252.2 FOS v-fos FBJ murine osteosarcoma viral oncogene
homolog NM_002072 GNAQ guanine nucleotide binding protein (G
protein), q polypeptide NM_002081.1 GPC1 glypican 1 NM_005896.2
IDH1 isocitrate dehydrogenase 1 (NADP+), soluble NM_002168.2 IDH2
isocitrate dehydrogenase 2 (NADP+), mitochondrial NM_021070.2 LTBP3
latent transforming growth factor beta binding protein 3
NM_002374.3 MAP2 microtubule-associated protein 2 NM_001012333.1
MDK midkine (neurite growth-promoting factor 2) NM_000435.1 NOTCH3
Notch homolog 3 (Drosophila) NM_002585 PBX1 pre-B-cell leukemia
transcription factor 1 NM_000920 PC pyruvate carboxylase
NM_005391.1 PDK3 pyruvate dehydrogenase kinase, isozyme 3
NM_002705.3 PPL periplakin NM_002737.2 PRKCA protein kinase C,
alpha NM_152880.2 PTK7 PTK7 protein tyrosine kinase 7 NM_001038.4
SCNN1A sodium channel, nonvoltage-gated 1 alpha NM_003355.2 UCP2
uncoupling protein 2 (mitochondrial, proton carrier) NM_003389
CORO2A coronin, actin binding protein, 2A NM_003568.1 ANXA9 annexin
A9 NM_003786.2 ABCC3 ATP-binding cassette, sub-family C (CFTR/MRP),
member 3 NM_004753.4 DHRS3 dehydrogenase/reductase (SDR family)
member 3 NM_005117.2 FGF19 fibroblast growth factor 19 NM_005759
ABI-2 abl-interactor 2 NM_002510 GPNMB glycoprotein (transmembrane)
nmb NM_006393 NEBL nebulette NM_007365 PADI2 peptidyl arginine
deiminase, type II NM_014988.1 LIMCH1 LIM and calponin homology
domains 1 NM_015132 SNX13 sorting nexin 13 NM_015271.2 TRIM2
tripartite motif-containing 2 NM_014467.1 SRPX2
sushi-repeat-containing protein, X-linked 2 NM_014067.2 MACROD1
MACRO domain containing 1 NM_013296.3 GPSM2 G-protein signalling
modulator 2 (AGS3-like, C. elegans) NM_016619.1 PLAC8
placenta-specific 8 NM_016233.1 PADI3 peptidyl arginine deiminase,
type III AK096661.1 DKFZP761M1511 hypothetical protein
DKFZP761M1511 NM_019027.1 RBM47 RNA binding motif protein 47
AF172820.1 MXRA6 matrix-remodelling associated 6 NM_032379.3 SYTL2
synaptotagmin-like 2 NM_017734.2 PALMD palmdelphin NM_017905.3
TMCO3 transmembrane and coiled-coil domains 3 NM_020169.2 LXN
latexin NM_020397.2 CAMK1D calcium/calmodulin-dependent protein
kinase ID AB007969.1 CLMN calmin (calponin-like, transmembrane)
NM_021180.2 GRHL3 grainyhead-like 3 (Drosophila) NM_022783.1 DEPDC6
DEP domain containing 6 NM_024539.3 RNF128 ring finger protein 128
NM_024896.2 KIAA1815 KIAA1815 NM_025094 FLJ22184 hypothetical
protein FLJ22184 NM_024491.2 CEP70 centrosomal protein 70 kDa
NM_024491 BITE p10-binding protein NM_030821 PLA2G12 phospholipase
A2, group XII NM_032042.3 C5orf21 chromosome 5 open reading frame
21 NM_032229.2 SLITRK6 SLIT and NTRK-like family, member 6
NM_032872.1 SYTL1 synaptotagmin-like 1 NM_177963.2 SYT12
synaptotagmin XII NM_138393.1 C19orf32 chromosome 19 open reading
frame 32 NM_080597.2 OSBPL1A oxysterol binding protein-like 1A
NM_199165.1 ADSSL1 adenylosuccinate synthase like 1
NM_138962 MSI2 musashi homolog 2 (Drosophila) NM_138801 LOC130589
aldose 1-epimerase NM_138801.1 GALM galactose mutarotase (aldose
1-epimerase) NM_144658.2 DOCK11 dedicator of cytokinesis 11
NM_152527.3 SLC16A14 solute carrier family 16 (monocarboxylic acid
transporters), member 14 NM_170743.2 IL28RA interleukin 28
receptor, alpha (interferon, lambda receptor) NM_001012642.1 GRAMD2
GRAM domain containing 2 NM_174921.1 C4orf34 chromosome 4 open
reading frame 34 NM_174921 LOC201895 hypothetical protein LOC201895
NM_207362.1 C2orf55 chromosome 2 open reading frame 55 NM_174921.1
C4orf34 chromosome 4 open reading frame 34 AA732944 RALGPS2 Ral GEF
with PH domain and SH3 binding motif 2 AU145501 NAALADL2
N-acetylated alpha-linked acidic dipeptidase- like 2 NM_006252.3
PRKAA2 protein kinase, AMP-activated, alpha 2 catalytic subunit
AW294903 WNT7B wingless-type MMTV integration site family, member
7B
[0032] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. Methods
and materials are described herein for use in the present
invention; other, suitable methods and materials known in the art
can also be used. The materials, methods, and examples are
illustrative only and not intended to be limiting. All
publications, patent applications, patents, sequences, database
entries, and other references mentioned herein are incorporated by
reference in their entirety. In case of conflict, the present
specification, including definitions, will control.
[0033] Other features and advantages of the invention will be
apparent from the following detailed description and figures, and
from the claims.
DESCRIPTION OF DRAWINGS
[0034] FIGS. 1A-H. Impact of IKKb-induced NF-kB on tumor rejection.
(a) EMSA showing NF-kB nuclear levels in LLC-OVA transduced with
control MiG, IKK and MiG treated with TNFa for 1 h and 2 h as
indicated. (b) RT-PCR showing KC/CXCL1 expression in LLC-OVA
transduced with control MiG, IKK and MiG treated with TNFa for 1 h
and 2 h as indicated. Samples run in triplicate and reported as
mean+/-SEM. (c) Tumor growth in C57B1/6 mice inoculated s.c. with
non-immunogenic LLC-MiG and LLC-IKK over indicated time periods.
Each line represents a single mouse. (d) Tumor growth in C57B1/6
mice inoculated s.c. with immunogenic LLC-OVA-MiG and LLC-OVA-IKK
over indicated time periods. Each line represents a single mouse.
(e) Impact of immunogenic-LLC tumors on peripheral T cells.
Tetramer analysis of OVA-specific CD8 T cells in peripheral blood
on day 10 from naive mice or mice receiving LLC-OVA-MiG or
LLC-OVA-IKK cells s.c. Each point represents a single mouse.
Student's T test was performed to compare tetramer positive CD8 T
cells between mice receiving LLC-OVA-MiG and LLC-OVA-IKK tumors.
(f) C57B1/6 mice received s.c. LLC-OVA-MiG or LLC-OVA-IKK and tumor
growth was monitored. Relative fold increase in tumor volume in
mice at D21 post-inoculation compared to D4 post-inoculation.
Combined results from 3 independent experiments are shown (n=11 for
both groups). Each point represents tumor growth from a single
mouse. (g) Tumor growth in RAG2-/- mice inoculated s.c. with
LLC-OVA-MiG or LLC-OVA-IKK. Each line represents a single mouse.
(h) Tumor growth in L129/sv mice inoculated s.c. with immunogenic
LKR-OVA-MiG and LKR-OVA-IKK over indicated time periods. Each line
represents a single mouse.
[0035] FIG. 1I. Impact of immunogenic-LLC tumors (LLC-OVA) on
peripheral OVA-specific CD8 T cells. Tetramer analysis showing
percent OVA-specific CD8 T cells in peripheral blood (out of total
CD8 T cells) on day 10 from mice receiving LLC-OVA s.c or naive
mice as indicated. Each point represents a single mouse. Student's
T test was performed to compare tetramer positive CD8 T cells
between naive mice and those receiving LLC-OVA tumors.
[0036] FIG. 1J. Activation of NF-kB in non-immunogenic LLC cells.
EMSA showing nuclear levels of NF-kB (top) and AP1 (bottom) in
LLC-MiG, LLC-IKK, or LLC-MiG cells treated with TNF.alpha.. Results
are representative of at least two independent experiments.
[0037] FIG. 1K. NF-kB and AP1 activity as determined by EMSA in
parental LKR-13, LKR-13 transduced with the control MiG retrovirus
(MiG), IkB.alpha.SR retrovirus (IkB.alpha.) and CA-IKK.beta.
retrovirus (IKK.beta.). Mobility of different complexes is
indicated with arrows.
[0038] FIGS. 2A-B. NF-kB enhances tumor rejection in
vaccine-activated T cell models. (a) Growth of LLC in a metastatic
model of lung cancer. H&E staining of lungs from mice 24 days
after receiving i.v. non-immunogenic LLC-MiG or LLC-IKK, or
immunogenic LLC-OVA-MiG or LLC-OVA-IKK. All scale bars represent 4
mm. (b) C57B1/6-BALB/c F1 (CB6) mice received s.c. TUBO-MiG or
TUBO-IKK. After 5 days, half of the mice in each group received
HER2 TriVax and tumor growth was monitored. Tumor growth of all
mice was calculated at D21 relative to D5 and relative growth in
vaccinated mice was compared to their unvaccinated counterparts.
p-value calculated using Student's T test with Welch's correction.
Graph shows combined results of 2 independent experiments, each
point represents tumor growth from a single mouse.
[0039] FIG. 2C. Presence of HER2-specific CD8 T cells after TriVax
administration. Tetramer analysis of peripheral blood on day 10
from naive mice or mice receiving TUBO-MiG or TUBO-IKK with or
without TriVax vaccination. Vaccination given on day 5. Each point
represents data from a single mouse. Results are representative of
two independent experiments. Student's T test was performed to
compare tetramer positive CD8 T cells between TriVax treated mice
receiving TUBO-MiG and those receiving TUBO-IKK tumors.
[0040] FIGS. 3A-D. Impact of T cell chemokines on tumor rejection.
(a) IFNg production by OT-1 CD8 T cells. ELISpot of OT-I T cells
cultured with LLC-parental, LLC-OVA, LLC-OVA-MiG, or LLC-OVA-IKK
tumor cells. 1x105 LLC targets/well. 1x105 T cells/well. Samples
were run in triplicate with results reported as mean+/-SEM. (b)
Affymetrix probe set signal intensity of indicated chemokines in
LLC-OVA-IKK compared to LLC-OVA-MiG. Genes identified in two
separate microarray experiments are shown, and reported as
mean+/-SEM. (c) RT-PCR showing CCL2 and CCL5 expression in LLC
parental, LLC transduced with OVA and LLC transduced with OVA+MiG
or OVA+IKK. Samples were run in triplicate and reported as
average+/-SEM. (d) LLC-OVA-IKK-Lenti control and LLC-OVA-IKK-CCL2
KD cells were injected s.c. in C57B1/6 mice and tumor growth was
monitored. Each line represents tumor growth in a single mouse. All
results are representative of at least two independent
experiments.
[0041] FIG. 3E. Impact of IKK.beta. expression on chemokine
expression in LLC-OVA determined by RNA microarray analysis on an
Affymetrix platform. Affymetrix probe set intensity fold increase
in indicated chemokine expression in LLC-OVA-IKK compared to
LLC-OVA-MiG. Genes identified in two separate microarray
experiments are shown, and reported as mean+/-SEM.
[0042] FIG. 3F. LLC-OVA-MiG and LLC-OVA-IKK supernatants were
collected 24 h after plating and the amount of secreted CCL2 was
determined by ELISA.
[0043] FIGS. 4A-E. Generation and validation of a lung cancer NF-kB
signature. (a) EMSA analysis of sorted H23, PC9 and HCC827 cells
infected with GFP, IkB or IKKb retroviruses as indicated. Parental
(Par) were not infected. The major NF-kB complexes are indicated
with arrows. (b) Determination of mRNA expression of NF-kB target
genes BIRC3 and TNFAIP3 in low NF-kB signature (H322, H1395, H522,
H1437) and high NF-kB signature (H226, H157, H1299, H650) cells.
Relative expression is shown after normalizing to 18s rRNA levels.
Samples were run in triplicate and reported as average+/-SEM.
Students t-test was used to determine statistical significance in
difference in mean expression of BIRC3 and TNFAIP3 in low versus
high cell lines. (c) H226, H157 and H1299 were transduced with MiG
or IkBaSR retroviruses following which BIRC3 and TNFAIP3 mRNA
expression was determined as in "b". (d) Expression of CCL2, CCL5,
CXCL1-3 and IL8 was determined in H157 cells transduced with MiG
and IkBaSR. (e) NF-kB activity determined by EMSA in NF-kB
signature low (1-4) and high (5-8) cell lines. Major NF-kB
complexes are indicated with arrows.
[0044] FIGS. 5A-D. Association of T cell chemokines, but not
neutrophil chemokines, with T cell presence in human lung
adenocarcinoma. (a) Correlation plot based on Spearman correlation
r-value of CMCLA gene expression data in human adenocarcinomas
(n=442) for T cell chemokines (CCL2, CCL5, CXCL10), neutrophil
chemokines (CXCL1-3, IL8), and T cell receptor (TRAC, TRBC1) genes.
Gene names and Affymetrix probe set ID numbers are shown. (b)
Correlation r-values of LTb expression with T cell chemokines,
neutrophil chemokines, and T cell presence in CMCLA dataset
(n=442). Gene name and Affymetrix probe set ID numbers for genes
with 2 probe sets are shown. (c) mRNA expression of indicated genes
normalized to 18s rRNA in HCC827 lung cancer cells determined by
RT-PCR. Fold difference in expression of genes after LT treatment
compared to untreated cells is shown. Samples were run in
triplicate and reported as mean+/-SEM. (d) mRNA expression of
indicated genes normalized to 18s rRNA in HCC827 lung cancer cells
determined by RT-PCR. Fold difference in expression of genes after
TNFa treatment compared to untreated cells is shown. Samples were
run in triplicate and reported as mean+/-SEM.
[0045] FIG. 5E. Correlation of expression between genes in human
lung adenocarcinoma. Correlation r-values of expression of
different NF-kB activating cytokine genes (y-axis) with expression
of different genes (x-axis). Gene name and Affymetrix probe set
numbers for genes with 2 probe sets are shown.
[0046] FIGS. 6A-D. Association of NF-kB signature inflammatory
genes with patient survival. (a-d) Association of mRNA expression
of indicated inflammatory genes (and Affymetrix probe set ID) with
OS in CMCLA dataset (n=442). 5-year OS of patients exhibiting high
versus low expression of indicated genes using a median cutoff.
Kaplan-Meier method was used to generate survival curves and the
log-rank test was used to test survival difference between the low
and high expression groups by median cutoff for each gene. The
p-value shown was adjusted by false discovery rate (FDR) for
multiple testing.
[0047] FIGS. 7A-E. Association of NF-kB signature immune response
genes and T cell presence with patient survival. (a-d) Association
of mRNA expression of indicated immune response genes (and
Affymetrix probe set ID) with OS in CMCLA (n=442). 5-year OS of
patients exhibiting high versus low expression of indicated genes
using a median cutoff. Kaplan-Meier method was used to generate
survival curves and the log-rank test was used to test survival
difference between the low and high expression groups by median
cutoff for each gene. The p value shown was adjusted by false
discovery rate (FDR) for multiple testing. (e) Association of T
cell presence detected by expression of T cell receptor genes
(TRAC, TRBC1) on patient survival was determined as with above
genes.
[0048] FIG. 8A. Correlation of expression between each of the ten
NF-.kappa.B driver genes (y-axis) and PC1 (top panels) and PC2
(bottom panels) of the NF-.kappa.B signature 159 probesets (x-axis)
in the CMCLA dataset. Correlation r-values, gene names and
Affymetrix probe set ID are shown.
[0049] FIG. 8B. The first 3 Principal Components (PC1-3) of the T
cell receptor genes (TRAC and TRBC1) (Top panel) or the ten-gene
NF-kB signature (bottom panel) are shown in the CMCLA dataset.
[0050] FIGS. 8C-G. Association of T cell presence with NF-kB
activity in human lung cancer. (c) Spearman correlation plot with
r-value of NF-kB signature (159 probe sets) PC1 with T cell PC1 are
shown for CMCLA data (n=442). (d) Correlation plot of NF-kB
signature (159 probe sets) PC1 with MR signature PC1. (e)
Correlation plot of NF-kB signature PC1 (159 probe sets) with
10-gene NF-kB signature PC1. (f) Correlation plot of T cell PC1
with 10-gene NF-kB signature PC1. (g) Correlation plot of MR
signature PC1 with 10-gene NF-kB signature PC1.
[0051] FIG. 8H. Correlation of expression between the 10-gene NF-kB
signature with T cell presence and the MR signature in the GSE14814
dataset (n=133). Correlation r-values are shown.
[0052] FIG. 9. Correlation plot of NF-kB signature (159 probe sets)
PC1 (top panel) or 10-gene NF-kB signature PC1 (bottom panel) with
T cell presence in the melanoma dataset GSE19234. Correlation
r-values are shown.
DETAILED DESCRIPTION
[0053] Previously, it was not known whether tumor NF-kB regulates T
cell-mediated anti-tumor responses and immune surveillance.
Nonetheless, consistent with known pro-tumor functions, it is
possible that NF-kB also impairs anti-tumor T cell responses
through cancer cell-intrinsic and/or microenvironment effects.
However, as shown herein, in immunogenic tumors in mice NF-kB
induces T cell-mediated tumor rejection. Enhanced T cell
recruitment was found to be a key NF-kB dependent mechanism for
tumor rejection. To investigate potential pro-tumor and anti-tumor
NF-kB functions in human cancer, a novel human lung cancer NF-kB
gene expression signature was developed. Although there was
evidence of both inflammatory and immune-response functions,
overall NF-kB activity was strongly associated with T cell presence
in human lung cancer; as T cell presence in tumors can be
associated with immune surveillance and improved patient survival,
NF-kB activity as determined by the gene signature described herein
is prognostic. These findings in both murine and human lung cancer
indicate that a crucial and previously unappreciated function of
tumor NF-kB is to promote T cell-mediated immune surveillance
responses; thus, the NF-kB gene signature can be used to select
subjects for treatment with immunotherapy.
[0054] As described herein, identification of genes that are
regulated by NF-kB specifically in lung cancer cells provide an
indicator of NF-kB activation state. Such an NF-kB signature can
then be used to predict disease outcome and survival, and validate
this pathway as a potentially crucial therapeutic target in human
lung cancer. To this end, an NF-kB signature was established in
lung cancer cell lines and the correlation between NF-kB activation
state and disease outcome and patient survival was determined using
the Consortium for the Molecular Classification of Lung
Adenocarcinoma (CMCLA) survival prediction study (Shedden et al.
2008. Nat Med 14:822-827).
[0055] Methods of Treating, Assigning a Prognosis or Predicting
Survival
[0056] The methods can be used to monitor a treatment (e.g., an
immunotherapy or administration of an NF-kB inhibitor), or to
select a treatment, e.g., to select a treatment regime including an
immunotherapy or administration of an NF-kB inhibitor for a
subject. In addition, the methods described herein can be used for,
e.g., to assist in, assigning a prognosis or predicting survival in
a subject who has a tumor, e.g., a solid tumor.
[0057] As used herein, the term "cancer" refers to cells having the
capacity for autonomous growth, i.e., an abnormal state or
condition characterized by rapidly proliferating cell growth.
Hyperproliferative and neoplastic disease states may be categorized
as pathologic, i.e., characterizing or constituting a disease
state, or may be categorized as non-pathologic, i.e., a deviation
from normal but not associated with a disease state. In general, a
cancer will be associated with the presence of one or more tumors,
i.e., abnormal cell masses. The term "tumor" is meant to include
all types of cancerous growths or oncogenic processes, metastatic
tissues or malignantly transformed cells, tissues, or organs,
irrespective of histopathologic type or stage of invasiveness.
"Pathologic hyperproliferative" cells occur in disease states
characterized by malignant tumor growth. In general, the methods
described herein can be practiced on subjects with solid
tumors.
[0058] Tumors include malignancies of the various organ systems,
such as affecting lung, breast, thyroid, lymphoid,
gastrointestinal, and genito-urinary tract, as well as
adenocarcinomas which include malignancies such as most colon
cancers, renal-cell carcinoma, prostate cancer and/or testicular
tumors, non-small cell carcinoma of the lung, cancer of the small
intestine and cancer of the esophagus. The term "carcinoma" is art
recognized and refers to malignancies of epithelial or endocrine
tissues including respiratory system carcinomas, gastrointestinal
system carcinomas, genitourinary system carcinomas, testicular
carcinomas, breast carcinomas, prostatic carcinomas, endocrine
system carcinomas, and melanomas. In some embodiments, the disease
is renal carcinoma or melanoma. Exemplary carcinomas include those
forming from tissue of the cervix, lung, prostate, breast, head and
neck, colon and ovary. The term also includes carcinosarcomas,
e.g., which include malignant tumors composed of carcinomatous and
sarcomatous tissues. An "adenocarcinoma" refers to a carcinoma
derived from glandular tissue or in which the tumor cells form
recognizable glandular structures. The term "sarcoma" is art
recognized and refers to malignant tumors of mesenchymal
derivation.
[0059] In some embodiments, cancers evaluated by the methods
described herein include those that are particularly immunogenic,
e.g., neuroblastoma, melanoma, and renal cell cancer.
[0060] In some embodiments, cancers or tumors evaluated by the
methods described herein include carcinomas (i.e., epithelial
cancers), such as a lung cancer (e.g., non-small-cell lung cancer
(NSCLC)), breast cancer, colorectal cancer, head and neck cancer,
or ovarian cancer. Epithelial malignancies are cancers that affect
epithelial tissues, and include adenocarcinomas and squamous cell
carcinoma. In some embodiments, the methods can be used to evaluate
or treat adenocarcinomas.
[0061] The present methods can include the use of some or all of
the genes shown in Table A or Table 1, e.g., 10, 20, 30, 40, 50,
60, 70, 80, 90, 100, 110, 120, 130, 138, 140, 150, 160, 170, 180,
190, or more of the genes in Table 1, e.g., the genes shown in
Table 2 or 3, or more preferably the genes in the ten-gene
signature (i.e., GBP1, PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8,
IL32, SH2B3, and NFKBIE), to predict NF-kB transcriptional activity
in different tumor types, predict outcome in subjects with tumors,
and select treatments for subjects with tumors. This can be done
using methods known in the art, e.g., using a classifier based on
principal component analysis (PCA), e.g., as described below; by a
classifier based on weighted majority voting; or classifiers based
on multi-dimensional scaling of the full 240 genes or a sub-set of
the genes described in this application or in U.S. Ser. No.
61/554,314 (Shedden et al., 2008. Nat Med 14:822-827; Chen et al.,
2010. Breast Cancer Res Treat 119:335-346). Those tumors found to
have high levels of NF-kB activity can then be selected for
treatment, e.g., with an NF-kB inhibitor, and NF-kB activator,
and/or with immunotherapy. Depending on levels of NF-kB activity
(high or low), subjects can be selected can be selected for
treatment, e.g., with an NF-kB inhibitor or with immunotherapy
(with high NF-kB activity) or compounds capable of enhancing NF-kB
to enhance effect of immunotherapy (with low NF-kB activity).
[0062] Further, five inflammatory genes (CXCL1, CXCL3, IL6, and
IL8) have been identified that predict poor outcome (see FIGS.
6A-D). These genes are thought to mediate inflammatory as well as
tumor invasion and metastasis responses. The methods can include
identifying subjects having tumors with high levels of expression
of those genes, and treating them with NF-kB pathway
inhibitors.
[0063] Additional NF-kB signature genes have been identified (CCL2,
ICAM-1, LTB, and CD83) predict improved survival (see FIGS. 7A-D).
High expression of these genes is expected to be associated with
improved response to cancer immunotherapy treatment, thus, the
methods can include identifying subjects having tumors with high
levels of expression of those genes, and treating them with
immunotherapy.
[0064] Finally, as demonstrated herein, LTB individually predicts
improved outcome (FIG. 7B) and the presence of T cells in tumors.
LTB induces expression of T cell chemokines such as CCL2 and CCL5
in lung cancer cells. High expression of LTB is expected to be
associated with improved response to cancer immunotherapy
treatment. Thus, the methods can include identifying subjects
having tumors with high levels of expression of LTB, and treating
them with immunotherapy.
[0065] The methods described herein generally include obtaining a
sample from a subject, and evaluating the presence and/or level of
an NF-kB signature gene in the sample, and comparing the presence
and/or level with one or more references, e.g., a control reference
that represents a normal level of the NF-kB signature gene, e.g., a
level in an unaffected subject, a level in a normal, non-tumor
tissue of the subject (e.g., normal lung tissues) and/or a disease
reference that represents a level associated with outcome or
survival. The methods can include detecting the level of a gene as
described herein, or a protein encoded by a gene described herein.
The presence and/or level of a gene or protein can be evaluated
using methods known in the art, e.g., using quantitative PCR
methods or molecular barcoding technologies, e.g., NanoString.TM.
(see, e.g., U.S. Pat. No. 7,919,237; U.S. Pat. No. 7,473,767; and
Geiss et al., Nature Biotechnology 26: 317-25 (2008)). In some
embodiments, high throughput methods, e.g., protein or gene chips
as are known in the art (see, e.g., Ch. 12, Genomics, in Griffiths
et al., Eds. Modern genetic Analysis, 1999, W. H. Freeman and
Company; Ekins and Chu, Trends in Biotechnology, 1999, 17:217-218;
MacBeath and Schreiber, Science 2000, 289(5485):1760-1763; Simpson,
Proteins and Proteomics: A Laboratory Manual, Cold Spring Harbor
Laboratory Press; 2002; Hardiman, Microarrays Methods and
Applications: Nuts & Bolts, DNA Press, 2003), can be used to
detect the presence and/or level of an NF-kB signature gene or gene
product.
[0066] In some embodiments, the methods include assaying levels of
one or more control genes or proteins, and comparing the level of
expression of the immune-related genes or proteins to the level of
the control genes or proteins, to normalize the levels of the
immune-related genes or proteins. Suitable endogenous control genes
includes a gene whose expression level should not differ between
samples, such as a housekeeping or maintenance gene, e.g., 18S
ribosomal RNA; beta Actin; Glyceraldehyde-3-phosphate
dehydrogenase; Phosphoglycerate kinase 1; Peptidylprolyl isomerase
A (cyclophilin A); Ribosomal protein L13a; large Ribosomal protein
P0; Beta-2-microglobulin; Tyrosine 3-monooxygenase/tryptophan
5-monooxygenase activation protein, zeta polypeptide; Succinate
dehydrogenase; Transferrin receptor (p90, CD71); Aminolevulinate,
delta-, synthase 1; Glucuronidase, beta; Hydroxymethyl-bilane
synthase; Hypoxanthine phosphoribosyltransferase 1; TATA box
binding protein; and/or Tubulin, beta polypeptide.
[0067] Generally speaking, the methods described herein can be
performed on cells from a tumor, e.g., a benign or malignant tumor.
The cells can be obtained by known methods, e.g., during a biopsy
(such as a core needle biopsy), or during a surgical procedure to
remove all or part of the tumor. The cells can be used fresh,
frozen, fixed, and/or preserved, so long as the mRNA or protein
that is to be assayed is maintained in a sufficiently intact state
to allow accurate analysis.
[0068] In some embodiments of the methods described herein, the
levels of the immune-related genes in the tumor sample can be
compared individually to levels in a reference. The reference
levels can represent levels in a subject who has a good outcome, a
good prognosis, or a long predicted survival time (e.g., 2 years or
more). Alternatively, reference levels can represent levels in a
subject who has a poor prognosis, or a shorter predicted survival
time (e.g., less than 2 years). In some embodiments, the reference
levels represent a threshold, and a level in the tumor that is
above the threshold reference level indicates that the subject has
a good outcome, a good prognosis, or a long predicted survival time
(e.g., 2 years or more), and levels below the threshold reference
level indicates that the subject has a poor outcome, a poor
prognosis, or a shorter predicted survival time (e.g., less than 2
years).
[0069] In some embodiments, the reference levels can represent
levels in a subject who is predicted to respond to immunotherapy.
Alternatively, reference levels can represent levels in a subject
who is predicted to have no or a poor response to immunotherapy. In
some embodiments, the reference levels represent a threshold, and a
level in the tumor that is above the threshold reference level
indicates that the subject is predicted to respond to
immunotherapy, and levels below the threshold reference level
indicates that the subject is predicted to have no or poor response
to immunotherapy. In subjects who are predicted respond to
immunotherapy, the methods can further include administering an
immunotherapy for those subjects, or selecting or recommending a
treatment including an immunotherapy for those subjects.
[0070] In some embodiments of the methods described herein, values
representing the levels of the NF-kB genes (or subsets thereof,
e.g., as described herein) can be summed to produce a "NF-kB gene
score" that can be compared to a reference NF-kB gene score,
wherein an NF-kB gene score that is above the reference NF-kB gene
score indicates that the subject has a long predicted survival time
(e.g., 2 years or more) or is predicted to have a positive response
to immunotherapy, and an NF-kB gene score below the reference score
indicates that the subject has a shorter predicted survival time
(e.g., less than 2 years), or is predicted to have no or a poor
response to immunotherapy. In subjects with an NF-kB gene score
below the reference score, an NF-kB activator can be administered
in conjunction with immunotherapy.
[0071] For example, in some embodiments, the expression levels of
each of the evaluated genes can be assigned a value (e.g., a value
that represents the expression level of the gene, e.g., normalized
to an endogenous control gene as described herein). That value
(optionally weighted to increase or decrease its effect on the
final score) can be summed to produce an immune-related gene score.
One of skill in the art could optimize such a method to determine
an optimal algorithm for determining an NF-kB gene score.
[0072] One of skill in the art will appreciate that references can
be determined using known epidemiological and statistical methods,
e.g., by determining an NF-kB gene score, or NF-kB gene-encoded
protein or mRNA levels, in tumors from an appropriate cohort of
subjects, e.g., subjects with the same type of cancer as the test
subject and a known prognosis (e.g., good or poor), immunotherapy
outcome, or predicted survival time (e.g., less than 2 years, or 2
years or more).
[0073] In some embodiments, the methods can be used to monitor the
efficacy of a treatment, e.g., an immunotherapy plus an NF-kB
activator. The methods include determining levels of the NF-kB
genes in a sample, then administering one or more doses of the
treatment, then determining levels of the NF-kB genes to determine
whether the treatment has increase immune infiltration of the
tumor. An increase in NF-kB gene levels (or immune-related gene
score, if calculated) indicates that the treatment was
effective.
[0074] Immunotherapy
[0075] T cell presence in tumors is typically associated with
immune surveillance and improved patient survival (Zhang et al.
2003. The New England Journal of Medicine 348:203-213; Fridman et
al., 2011. Cancer research 71:5601-5605; Pages et al. 2005. The New
England journal of medicine 353:2654-2666; Yu et al., 2009. Nature
reviews. Cancer 9:798-809; Yu et al., 2007. Nature Reviews.
Immunology 7:41-51; Schreiber et al., 2011. Science 331:1565-1570;
Vesely et al., 2011. Annual Review of Immunology 29:235-271).
Consequently, immunotherapy using blockade of negative regulators
of T cells function is an especially attractive approach (Hodi et
al., The New England journal of medicine 2010 363:711-723; Topalian
et al., 2012. The New England Journal of Medicine 366:2443-2454).
Unlike genetic lesion-specific therapies, immunotherapy has
potential for targeting tumors irrespective of driver oncogene
mutation status. Thus in some embodiments, the methods include
administering an immunotherapy to the subject, e.g., one or more
therapies that promote anti-cancer immunity, including
administering one or more of: dendritic cells or peptides with
adjuvant, immune checkpoint inhibitors, DNA-based vaccines,
cytokines (e.g., IL-2), cyclophosphamide, agonists of OX40 (OX40;
CD134), anti-interleukin-2R immunotoxins, and/or antibodies such as
anti-CD137, anti-PD1, or anti-CTLA-4; see, e.g., Kruger et al.,
Histol Histopathol. 2007 Jun.; 22(6):687-96; Eggermont et al.,
Semin Oncol. 2010 October; 37(5):455-9; Klinke D J 2nd, Mol Cancer.
2010 Sep. 15; 9:242; Alexandrescu et al., J Immunother. 2010
July-August; 33(6):570-90; Moschella et al., Ann N Y Acad Sci. 2010
April; 1194:169-78; Ganesan and Bakhshi, Natl Med J India. 2010
Jan.-Feb.; 23(1):21-7; Golovina and Vonderheide, Cancer J. 2010
Jul.-Aug.; 16(4):342-7; Hodi et al., The New England journal of
medicine 2010 363:711-723; Pentcheva-Hoang et al., Immunological
Reviews 2009 229:67-87; Brahmer et al., Journal of Clinical
Oncology 2010 28:3167-3175; Lynch et al., Journal of Clinical
Oncology 2012 30(17):2046; Weber, Current Opinion in Oncology 2011
23:163-169; Weber, Seminars in Oncology 2010 37:430-439; Topalian
et al., 2012. The New England Journal of Medicine 366:2443-2454;
and Higano et al., Cancer 2009 115:3670-3679. In some embodiments,
the methods include administering a composition comprising
tumor-pulsed dendritic cells, e.g., as described in WO2009/114547
and references cited therein. Additional examples of
immunotherapies include virus-based anti-cancer vaccines (e.g.,
adenovirus), formulations of Toll-like Receptor or RIG-I-like
receptor ligands, Adoptive T cell therapy or other cell types. In
some embodiments the immunotherapy is selected from the group
consisting of BiovaxID (an autologous vaccine containing
tumor-specific idiotype proteins from individual patient's lymphoma
cells conjugated to keyhole limpet hemocyanin (KLH)); Provenge
sipuleucel-T (an FDA-approved example of the use of autologous
dendritic cells); Yervoy (a mAb against CTLA-4 (CD152), approved in
2011 for metastatic melanoma); tremelimumab (formerly ticilimumab,
an anti-CTLA-4 mAb); IMA901 (a vaccine containing 10
tumor-associated peptides (TUMAPs)), alone or in combination with
Sutent (a small molecule VEGF receptor tyrosine kinase inhibitor);
GV1001 (a peptide vaccine with the sequence of human telomerase
reverse transcriptase (hTERT), from Kael-Gemvax); Lucanix
belagenpumatecel-L (four NSCLC cell lines carrying antisense
oligonucleotides against transforming growth factor beta 2
(TGFB2)); Stimuvax (a liposomal vaccine containing a synthetic
25-amino acid peptide sequence from mucin 1 (MUC1; CD227));
Allovectin velimogene aliplasmid (a DNA plasmid encoding major
histocompatibility complex (MHC) class I B7 (HLA-B7) complexed with
lipid); BMS-936558 (ONO-4538) (a human mAb against PD-1);
BMS-936559 (formerly MDX-1105) (a human mAb against PD-L1);
Zelboraf (vemurafenib, an oral small molecule inhibitor of the
oncogenic BRAF V600E mutation); Votrient (pazopanib, a small
molecule VEGF receptor tyrosine kinase inhibitor); ISF35 or
Lucatumumab (HCD122) (mAbs against CD40); GVAX (an allogeneic
cancer vaccine engineered to secrete granulocyte macrophage-colony
stimulating factor (GM-CSF)). See, e.g., Flanagan, "Immune
Springboard," Biocentury, Jun. 18, 2012 A5-A10 (2012), available at
biocentury.com. In some embodiments, the immunotherapy comprises
administration of an agent that effects CTLA4 blockade (e.g.,
Ipilumumab BMS), PD1-blockade (e.g., BMS-936558, BMS; CT-011,
Curetech; MK-3475, Merck), CD137 activation (e.g., BMS-663513,
BMS), PD-L1 blockade (e.g., BMS-936559, BMS), CD40 activation
(e.g., CP-870893, Pfizer) and autologous dendritic cells (e.g.,
Provenge).
[0076] NF-kB Inhibitors
[0077] In some embodiments, the methods include administering a
therapy comprising an NF-kB inhibitor, i.e., a compound that
inhibits the Nuclear Factor kappa B (NF-kB) intracellular
transcription factor, to a subject who has a high level of NF-kB
activity. Exemplary NF-kB inhibitors include sulfasalazine,
Luteolin, rapamycin or derivatives (e.g., temsirolimus and
everolimus), caffeic acid phenethylester, SN50 (a cell-permeable
inhibitory peptide), parthenolide, triptolide, wedelolactone,
lactacystin, substituted resorcinols,
(E)-3-(4-methylphenylsulfonyl)-2-propenenitrile (e.g., Bay 11-7082,
Bay 11-7821, or Bay 11-7085, Sigma-Aldrich, St. Louis, Mo.),
Pranlukast, etoposide, bortezomib, MLN9708 (Kupperman et al.,
MLN9708), PS-1145 (Millennium Pharmaceuticals),
tetrahydrocurcuminoids (such as Tetrahydrocurcuminoid CG, available
from Sabinsa Corporation of Piscataway, N.J.), extracts of
Paulownia tomentosa wood, and MG-132 [Z-Leu-Leu-Leu-H]. See, e.g.
U.S. Pat. No. 7,838,513. Inhibitory nucleic acids targeting NF-kB,
e.g., siRNA, antisense, or locked nucleic acids, can also be used.
Hsp90 inhibitors such as 17-DMAG, AUY-922 and IPI-504 can also be
used to inhibit NF-kB activation. See, e.g., US20070110828.
[0078] NF-kB Activators
[0079] In some embodiments, the methods include administering a
therapy comprising an NF-kB activator, i.e., a compound that
enhances activity of the Nuclear Factor kappa B (NF-kB)
intracellular transcription factor, to a subject who has a low
level of NF-kB activity, to enhance the anti-tumor immune response,
e.g., in combination with immunotherapy. Exemplary NF-kB activators
include cytotoxic anticancer agents such as taxanes (e.g.,
paclitaxel), vinca alkaloids (e.g., vinrelobine, vinblastine,
vindesine, and vincristine), anthracyclines (e.g., daunorubicin,
doxorubicin, mitoxantraone, and bisanthrene), epipodophyllotoxins
(e.g., etoposide, etoposide orthoquinone, and teniposide); histone
deacetylase inhibitors (e.g., romidepsin); pemetrexed; and
topoisomerase inhibitors. See, e.g., Das and White, 1997. The
Journal of Biological Chemistry, 272, 14914-14920; Nakanishi and
Toi, Nat Rev Cancer. 2005 Apr.; 5(4):297-309; Ganapathi et al.,
Curr Pharm Des. 2002; 8(22):1945-58; see also Goodman and Gilman's
The Pharmacological Basis of Therapeutics, e.g., 1277-1290 (7th ed.
1985) for descriptions and exemplary compounds.
EXAMPLES
[0080] The invention is further described in the following
examples, which do not limit the scope of the invention described
in the claims.
Example 1
Critical Role of NF-kB in Immunogenic Tumor Rejection in Mice
[0081] The presence of tumor infiltrating T cells, which likely
recognize tumor expressed antigens, is associated with improved
patient survival (Zhang et al. 2003. The New England Journal of
Medicine 348:203-213; Fridman 2011. Cancer Research 71:5601-5605;
Pages et al., 2005. The New England Journal of Medicine
353:2654-2666). To induce de novo anti-tumor T cell responses in
mice, Kb-OVA (a single polypeptide encoding H-2K.sup.b,
.beta..sub.2-M and the ovalbumin SIINFEKL peptide recognized by CD8
T cells)(Wang et al., 2009. Science 326:871-874) was expressed in
poorly immunogenic Lewis lung carcinoma (LLC) to generate
LLC-OVA.
[0082] The LLC cells were cultured in DMEM supplemented with 10%
fetal bovine serum (FBS). Retroviruses were prepared by
transfecting HEK 293T cells with Kb-OVA and packaging vectors as
previously described (Valenzuela et al., 2009. J Clin Invest
119:3774-3786). Retrovirus transduced cells were sorted based on
GFP expression using a FACS Vantage sorter (BD Biosciences, San
Jose, Calif.) (Wang et al., 2007. J Immunol 178:6777-6788).
Retrovirus infected LLC and human cell-lines were sorted based on
GFP expression to yield >95% purity. Tetramer staining was
performed as described (Cho and Celis, 2009. Cancer research
69:9012-9019) with the following changes: cells were incubated for
5 minutes at RT with Fc block and DAPI was added to cells prior to
analysis for viability gating. Her/Neu tetramer has been described
(Nava-Parada et al., 2007. Cancer research 67:1326-1334) and H2-Kb
OVA tetramer was purchased from Beckman Coulter (Brea, Calif.).
Flow cytometric analysis was performed on an LSR II cytometer (BD
Biosciences, San Jose, Calif.). Aggregates and dead cells were
excluded from analysis. Data were acquired using CellQuest software
(BD Biosciences, San Jose, Calif.) and analyzed using FlowJo
software (Tree Star, Ashland, Oreg.).
[0083] Cells were harvested in logarithmic growth after being
cultured for less than two weeks and washed once in injection
medium (phenol-free DMEM supplemented with 2% FBS) and counted.
5.times.10.sup.5 LLC cells were injected either s.c. (in a volume
of 100 ul) or i.v. (in a volume of 200 ul). Subcutaneous tumors
were monitored for growth and measured 2-3 times per week. Mice
receiving intravenous LLC injections were monitored for morbidity.
Mice were sacrificed when s.c. tumors reached a diameter of 20 mm
or when they showed signs of morbidity (i.v. or s.c.). Tumor volume
was calculated as previously described (Torabian et al. 2009. The
American Journal of Pathology 174:1009-1016). Relative tumor growth
between treatment groups was analyzed using the Student's T test
with Welch's correction. Mice were maintained under specific
pathogen free conditions.
[0084] Subcutaneous (s.c.) inoculation with LLC-OVA was sufficient
to induce an OVA-specific CD8 T cell response (FIG. 1I).
[0085] The next experiments determined how tumor NF-kB activity
impacts anti-tumor CD8 T cell responses. To selectively activate
NF-kB in tumor cells, tumor cell-specific expression of
constitutively-activated (CA)-IKK.beta. was utilized. IKK.beta.
mediates NF-kB activation in response to multiple stimuli and
pathways, including those activated by oncogenes such as KRAS
(Basseres et al., 2010. Cancer Res 70:3537-3546). Furthermore,
IKK.beta. is potentially amplified in human cancer (Beroukhim et
al., 2010. Nature 463:899-905). Retroviruses were prepared by
transfecting HEK 293T cells with MiG or activated IKK.beta. (S177,
S181 to E mutations; IKK.beta.EE) and packaging vectors as
previously described (Valenzuela et al., 2009. J Clin Invest
119:3774-3786; Wang et al., 2007. J Immunol 178:6777-6788).
[0086] CA-IKK.beta. expression in LLC (LLC-IKK) led to increased
NF-kB, but not AP-1, nuclear translocation (FIG. 1J). Similarly,
CA-IKK.beta. expression in LLC-OVA (LLC-OVA-IKK) enhanced nuclear
NF-kB, comprising primarily of RelA/p65-containing complexes, and
target gene CXCL1/KC expression (FIGS. 1A-B). However, both were
induced substantially less compared to TNF.alpha. treatment (FIGS.
1A-B). Therefore, CA-IKK.beta. induces modest activation of NF-kB
relative to TNF.alpha..
[0087] Compared to control LLC-MiG, LLC-IKK had no significant
effect on s.c. growth of non-immunogenic LLC in syngeneic C57BL/6
mice (FIG. 1C). Interestingly, LLC-OVA-IKK tumors initially grew
but were subsequently rejected while LLC-OVA-MiG grew unrestrained
(FIG. 1D). Importantly, a similar number of activated OVA-specific
CD8 T cells were detected in peripheral blood of LLC-OVA-MiG and
LLC-OVA-IKK mice (FIG. 1E) suggesting that reduced growth of
LLC-OVA-IKK was not due to impaired T cell priming Combined results
from 3 experiments indicated that once tumors were perceptible (day
4), 10/11 LLC-OVA-MiG showed 2-fold or greater tumor growth while
only 3/11 LLC-OVA-IKK showed similar growth (FIG. 1F). The
difference in tumor numbers showing growth in the two groups was
significant (p=0.008, Fisher's Exact Test). Importantly,
LLC-OVA-IKK grew robustly in Rag2-/- mice (FIG. 1G), demonstrating
a role for lymphocytes in rejection of LLC-OVA-IKK.
[0088] To extend these studies to a different lung tumor model,
KRAS mutant LKR-13 cells (DuPage et al., 2011. Cancer cell
19:72-85) were used. As in LLC, CA-IKK.beta. expression in LKR-13
also resulted in NF-kB activation (FIG. 1L). Importantly, while
LKR-OVA-MiG showed robust growth, LKR-OVA-IKK tumor growth was
drastically reduced (FIG. 1H). These results therefore indicate
that NF-kB activation induces rejection and/or growth suppression
of immunogenic lung tumors in mice.
[0089] The next experiments used a metastatic model in which i.v.
injected LLC form tumor foci in lungs. Importantly, LLC-OVA-IKK
showed virtually no tumor foci compared to LLC-OVA-MiG, while both
non-immunogenic LLC-MiG and LLC-IKK showing multiple foci (FIG.
2A). While few tumor cells were evident in lungs of LLC-OVA-IKK,
multiple lymphoid aggregates were detected.
[0090] The effect of NF-kB activation was then determined in
vaccine-induced responses against the breast carcinoma TUBO line,
which expresses HER2/neu (Lee et al., 2010. Cancer immunology,
immunotherapy: CII 59:1073-1081). Turin-Bologna (TUBO) cell-lines
were cultured in DMEM supplemented with 10% fetal bovine serum
(FBS), and transduced with the vectors as described above. TUBO-MiG
and TUBO-IKK injected mice were randomly split into control (no
vaccine) and TriVax (Assudani et al., 2008. Cancer Res.
68:9892-9899; Cho and Celis. 2009. Cancer Res. 69:9012-9019)
vaccine groups, which were immunized using a synthetic peptide from
HER2/neu (Nava-Parada et al., 2007. Cancer Res. 67:1326-1334). Mice
receiving TUBO cells were split into TriVax treatment and
non-treatment groups on day 5 Immunization with HER2 TriVax was
performed as described (Nava-Parada et al., 2007. Cancer Res.
67:1326-1334).
[0091] TriVax-induced HER2-specific T cell increase in peripheral
blood was similar in the two groups (FIG. 2C). Tumor growth of
vaccinated TUBO-MiG and TUBO-IKK was then determined relative to
their unvaccinated counterparts. While vaccination reduced growth
of both TUBO-MiG and TUBO-IKK tumors, the reduction was
significantly more pronounced in TUBO-IKK tumors (p=0.025) (FIG.
2B). Thus, NF-kB activation restrains tumor growth in de novo and
vaccine-induced T cell models.
Example 2
NF-kB Induced T Cell Chemokine Expression is Crucial for Tumor
Rejection
[0092] Possible mechanisms involved in rejection of LLC-OVA-IKK
tumors were investigated. Rejection was not likely mediated by
increased numbers of OVA-specific CD8 T cells (FIG. 1E). In
addition, LLC-OVA-IKK cells were not superior stimulators of CD8 T
cell IFN.gamma. expression (FIG. 3A). Importantly, while
LLC-OVA-MiG tumors had a small number of infiltrating CD8 T cells,
LLC-OVA-IKK tumors showed greatly increased CD8 T cell presence.
These results suggested that NF-kB regulated expression of T cell
chemokines may be responsible for increased T cell recruitment.
[0093] To identify T cell chemokines involved, global RNA
expression studies using microarray analysis was performed on
LLC-OVA-MiG and LLC-OVA-IKK. RNA was isolated using a Qiagen RNeasy
kit, then reverse-transcribed and subjected to quantitative PCR
analysis as described (Wang et al., 2007. J Immunol 178:6770-6776)
in an Applied Biosystems 7900HT Sequence Detection System
(Carlsbad, Calif.) with SYBR Green I Master Mix (Applied
Biosystems, Carlsbad, Calif.) using gene-specific primers. All
samples were run in triplicate and were normalized to rRNA 18s or
.beta.-actin. Primers were obtained from RealTimePrimers.com. For
microarray analysis the mRNA in 100 ng of total RNA was
specifically converted to cDNA and then amplified and labeled with
biotin using the Ambion Message Amp Premier RNA Amplification Kit
(Life Technologies, Grand Island, N.Y.) following the
manufacturer's protocol. Affymetrix Mouse Genome 430 2.0 Arrays
were used in these studies. Hybridization with the biotin-labeled
RNA, staining, and scanning of the arrays following the prescribed
procedure outlined in the Affymetrix technical manual. Results were
analyzed using the MAS 5.0 algorithm. Genes were considered changed
if they were identified as changed in the MAS 5.0 comparison
analysis and there was a 2-fold difference in signal compared to
the control condition.
[0094] A 2-fold cutoff was used to identify genes up-regulated or
down-regulated by IKK.beta. in two independent experiments. In
total, 88 genes were up-regulated and 83 genes were down-regulated
in both experiments. These included multiple chemokines involved in
both T cell and neutrophil chemotaxis (FIG. 3B and FIG. 3E).
Amongst T cell chemokines identified, CCL2, CCL5 and CXCL10 are
known to mediate activated T cell chemotaxis (Bromley et al., 2008.
Nature immunology 9:970-980). RT-PCR confirmed upregulation of CCL2
and CCL5 in LLC-OVA-IKK (FIG. 3C). Similarly, CCL2 and CCL5
expression was also substantially enhanced in LKR-OVA-IKK (data not
shown). shRNA mediated knock-down (KD) showed that RelA plays a
more important role than cRel or RelB in expression of T cell
chemokines in LLC-OVA-IKK.
[0095] CCL2 in particular exhibited a dramatically higher
microarray probe set signal than T cell chemokines CCL5 and CXCL10
(FIG. 3B) suggesting it may dominate T cell recruitment responses
by LLC-OVA-IKK. In addition, CCL2 protein expression was greatly
increased in LLC-OVA-IKK compared to LLC-OVA-MiG (FIG. 3F), as
determined by ELISA using the following methods. Supernatant was
collected from LLC-OVA-MiG cells and LLC-OVA-IKK cells after 24 h
of culture. Anti-CCL2 ELISA was performed to measure CCL2
production in cells and to confirm CCL2 knockdown using the Mouse
CCL2 ELISA Ready-SET-Go.RTM. kit from eBioscience (San Diego,
Calif.) according to the manufacturer's instructions.
[0096] Previous studies have indicated an important role for CCL2
in breast cancer metastasis through monocyte recruitment (Qian et
al., 2011. Nature 475:222-225) but also in T cell recruitment
(Harlin et al., 2009. Cancer research 69:3077-3085). Given high
expression and potentially diverse functions, it was determined
whether CCL2 was specifically required for rejection of LLC-OVA-IKK
tumors by KD of CCL2 expression. CCL2-KD did not impact LLC-OVA-IKK
growth in vitro or OVA-specific T cell expansion in vivo. However,
while control LLC-OVA-IKK tumors were readily rejected, CCL2-KD
resulted in robust tumor growth (FIG. 3D). These results therefore
suggest that CCL2 is a potentially crucial immune surveillance
regulating NF-kB target gene that is required for LLC-OVA-IKK
rejection.
Example 3
Generation of a Gene Expression Signature to Predict NF-kB Activity
in Human Lung Cancer
[0097] Given the above findings in mice, it was next determined
whether NF-kB activity is also associated with T cell presence in
human lung cancer. It was hypothesized that an NF-kB-driven gene
expression signature will provide a superior indication of NF-kB
activity than activation state of individual subunits or NF-kB
pathway kinases. Furthermore, previous studies have shown the
predictive potential of gene expression signatures in determining
pathway activation state (Bild et al. 2006. Nature 439:353-357;
Chang et al. 2009. Mol Cell 34:104-114). To the best of the present
inventors' knowledge, no such gene expression signature exists to
predict NF-kB activity in human lung cancer.
[0098] Five human lung cancer cell-lines (A549, H23, H358, PC9 and
HCC827) were used to generate such a signature. In each cell-line,
genes were identified that were impacted by NF-kB inhibition (using
retrovirus-expressed IkB.alpha.SR "super-repressor" of NF-kB) (Wang
et al., 2007. J Immunol 178:6770-6776) and/or NF-kB activation
(using CA-IKK.beta. described above) (Valenzuela et al. 2009. J
Clin Invest 119:3774-3786).
[0099] As shown in FIG. 4A, IkB.alpha.SR reduced and CA-IKK.beta.
increased NF-kB heterodimer nuclear activity compared to control
MiG retrovirus infected cells. Although NF-kB is widely considered
to be a tumor growth-promoting transcription factor, there was
surprisingly little effect on survival or proliferation of these 5
lung cancer cell-lines following NF-kB inhibition by IkB.alpha.SR
expression.
[0100] RNA isolated from these 5 cell-lines was used to perform
gene expression studies using Affymetrix U133 Plus 2.0 microarrays.
Initially, all probe sets impacted 1.4-fold or more by IkB.alpha.
or IKK.beta. relative to the control vector were identified in each
cell-line. This low stringency cutoff was used to accommodate a
second selection criteria. Next, the 5 cell-line signatures were
used to identify probe sets similarly regulated by IkB.alpha. and
IKK.beta. (with IkB.alpha. and IKK.beta. causing opposite effects
on gene expression) in the different cell lines in .gtoreq.60% of
the experimental conditions (i.e. co-regulation score of
.gtoreq.0.6). A total of 240 probe sets were identified using this
approach (Table 1). The rationale for this was that the
identification of genes similarly regulated by NF-kB in multiple
cell lines will not only eliminate false positives but also
generate a signature that is broadly applicable. As examples, 2
probe sets for the well-known NF-kB target gene BIRC3/cIAP2 (Chu et
al., 1997. Proc Natl Acad Sci USA 94:10057-10062; Wang et al.,
1998. Science 281:1680-1683) had co-regulation scores of 1.0 and
0.8, and both probe sets of another known NF-kB target gene
TNFAIP3/A20 (Harhaj and Dixit, 2011. Cell research 21:22-39) had
co-regulation scores of 1.0. In addition, IkB.alpha. increased
expression of certain genes while IKK.beta. repressed their
expression; these likely represent genes negatively impacted by
NF-kB activity. In the 240 probe sets (Table 1), .about.200
individual genes were present that were either up-regulated or
down-regulated by NF-kB.
TABLE-US-00002 TABLE 1 Up/Down Regulated Probe ID GenBank ID
GeneSymbol Up 209099_x_at NM_000214.1 JAG1 Up 216268_s_at
NM_000214.1 JAG1 Up 202076_at NM_001166.3 BIRC2 Up 210538_s_at
NM_182962.1 BIRC3 Up 230499_at NM_001165 BIRC3 Up 225285_at
NM_005504.4 BCAT1 Up 202357_s_at NM_001710.4 BF Up 217767_at
NM_000064.1 C3 Up 226016_at NM_001025079.1 CD47 Up 211075_s_at
NM_001025079.1 CD47 Up 213857_s_at NM_001025079.1 CD47 Up 227259_at
NM_001025079.1 CD47 Up 202902_s_at NM_004079.3 CTSS Up 221903_s_at
NM_015247 CYLD Up 203725_at NM_001924.2 GADD45A Up 224833_at
NM_005238.2 ETS1 Up 1555355_a_at NM_005238.2 ETS1 Up 202269_x_at
NM_002053.1 GBP1 Up 231577_s_at NM_002053.1 GBP1 Up 204224_s_at
NM_001024070.1 GCH1 Up 223278_at NM_004004.3 GJB2 Up 204470_at
NM_001511.1 CXCL1 Up 209774_x_at NM_002089.1 CXCL2 Up 207850_at
NM_002090.2 CXCL3 Up 208729_x_at NM_005514.5 HLA-B Up 209140_x_at
NM_005514.5 HLA-B Up 211911_x_at AY732487.1 HLA-C Up 202637_s_at
NM_000201.1 ICAM1 Up 215485_s_at NM_000201.1 ICAM1 Up 202638_s_at
NM_000201.1 ICAM1 Up 205207_at NM_000600.1 IL6 Up 204863_s_at
NM_002184.2 IL6ST Up 211000_s_at NM_002184.2 IL6ST Up 212195_at
NM_002184 IL6ST Up 212196_at NM_002184 IL6ST Up 202859_x_at
NM_000584.2 IL8 Up 207536_s_at NM_001561.4 TNFRSF9 Up 211786_at
NM_001561.4 TNFRSF9 Up 210511_s_at NM_002192.2 INHBA Up 231779_at
NM_001570.3 IRAK2 Up 202531_at NM_002198.1 IRF1 Up 205032_at
NM_002203.2 ITGA2 Up 227314_at NM_002203 ITGA2 Up 201389_at
NM_002205.2 ITGA5 Up 205786_s_at NM_000632.3 ITGAM Up 202351_at
NM_002210.2 ITGAV Up 202267_at NM_005562.1 LAMC2 Up 207517_at
NM_018891.1 LAMC2 Up 207339_s_at NM_009588.1 LTB Up 203936_s_at
NM_004994.2 MMP9 Up 209239_at NM_003998.2 NFKB1 Up 209636_at
NM_002502.2 NFKB2 Up 207535_s_at NM_002502.2 NFKB2 Up 203927_at
NM_004556.2 NFKBIE Up 1553994_at NM_002526 NT5E Up 201981_at
NM_002581.3 PAPPA Up 206034_at NM_198833.1 SERPINB8 Up 214866_at
NM_001005376.1 PLAUR Up 209040_s_at NM_004159.4 PSMB8 Up 204279_at
NM_002800.4 PSMB9 Up 201762_s_at NM_002818.2 PSME2 Up 206157_at
NM_002852.2 PTX3 Up 205205_at NM_006509.2 RELB Up 213194_at
NM_002941.2 ROBO1 Up 216598_s_at NM_002982.3 CCL2 Up 1405_i_at
NM_002985.2 CCL5 Up 205476_at NM_004591.1 CCL20 Up 202307_s_at
NM_000593.5 TAP1 Up 225973_at NM_000544.3 TAP2 Up 204769_s_at
NM_000544.3 TAP2 Up 208829_at NM_003190.3 TAPBP Up 204924_at
NM_003264.3 TLR2 Up 202643_s_at NM_006290.2 TNFAIP3 Up 202644_s_at
NM_006290.2 TNFAIP3 Up 205599_at NM_005658.3 TRAF1 Up 235116_at
NM_005658.3 TRAF1 Up 208315_x_at NM_003300.2 TRAF3 Up 203299_s_at
NM_003916 AP1S2 Up 222549_at NM_021101.3 CLDN1 Up 218182_s_at
NM_021101.3 CLDN1 Up 203828_s_at NM_001012638.1 IL32 Up 204440_at
NM_001040280.1 CD83 Up 221841_s_at NM_004235.3 KLF4 Up 212636_at NM
206853.1 QKI Up 204702_s_at NM_004289.5 NFE2L3 Up 236471_at
NM_004289 NFE2L3 Up 204589_at NM_014840.2 NUAK1 Up 205100_at
NM_005110.1 GFPT2 Up 1559064_at NM_005124 NUP153 Up 20320_at
NM_005475.1 SH2B3 Up 229564_at NM_005493 RANBP9 Up 201489_at
NM_005729.3 PPIF Up 202073_at NM_001008211.1 OPTN Up 202074_s_at
NM_001008211.1 OPTN Up 207196_s_at NM_006058.2 TNIP1 Up 204341_at
NM_006470.3 TRIM16 Up 213667_at NM_006662 SRCAP Up 220091_at
NM_017585.2 SLC2A6 Up 227846_at BC067106.1 GPR176 Up 218644_at
NM_016445.1 PLEK2 Up 213524_s_at NM_015714.2 G0S2 Up 220054_at
NM_016584.2 IL23A Up 209788_s_at NM_001040458.1 ARTS-1 Up
218834_s_at NM_178031.2 HSPA5BP1 up 218627_at NM_018370.1 DRAM Up
219901_at NM_018351.2 FGD6 Up 220658_s_at NM_020183.3 ARNTL2 Up
224204_x_at NM_020183.3 ARNTL2 Up 222001_x_at XM_934503.1 FAM91A2
Up 229429_x_at XM_934505.1 FAM91A2 Up 209267_s_at NM_022154.5
SLC39A8 Up 219209_at NM_022168.2 IFIH1 Up 219759_at NM_022350.1
ERAP2 Up 218543_s_at NM_022750.2 PARP12 Up 222692_s_at NM_022763.2
FNDC3B Up 229865_at BC012204.1 FNDC3B Up 1568609_s_at NM_024615.2
PARP8 Up 219033_at NM_024615.2 PARP8 Up 220987_s_at NM_030952.1
NUAK2 Up 54970_at NM_031449.3 ZMIZ2 Up 223484_at NM_032413.2
C15orf48 Up 204823_at NM_014903.3 NAV3 Up 232593_at NM_138397
LOC93082 Up 220975_s_at NM_030968.2 C1QTNF1 Up 240770_at NM_173490
LOC134285 Up 205673_s_at NM_001031739.1 ASB9 Up 227599_at
NM_178496.2 C3orf59 Up 226725_at NM_144975 MGC19764 Up 241379_at
NM_173545.1 C2orf13 Up 212989_at NM_147156.3 TMEM23 Up 225105_at
NM_207376.1 OCC-1 Up 229872_s_at XM_498811.2 KIAA0493 Up 229264_at
XM_930678.1 LOC642441 Up 1555673_at XM_937100.1 LOC728285 Up
202660_at NM_002223.2 ITPR2 Up 227140_at NM_002192.2 INHBA Up
232060_at AK000776 ROR1 Up 232797_at AU144005 ITGAV Up 230787_at
AW197616 Up 236704_at BG413366 Down 202053_s_at NM_000382.2 ALDH3A2
Down 202054_s_at NM_000382.2 ALDH3A2 Down 209442_x_at NM_020987.2
ANK3 Down 231729_s_at NM_004058.2 CAPS Down 231728_at NM_004058.2
CAPS Down 211922_s_at NM_001752.2 CAT Down 204920_at NM_001875.2
CPS1 Down 217564_s_at NM_001875.2 CPS1 Down 202295_s_at NM_004390.2
CTSH Down 209782_s_at NM_001352.2 DBP Down 215116_s_at
NM_001005336.1 DNM1 Down 37996_s_at NM_004409.2 DMPK Down 204540_at
NM_001958.2 EEF1A2 Down 36499_at NM_001408.1 CELSR2 Down 212339_at
NM_177996.1 EPB41L1 Down 209189_at NM_005252.2 FOS Down 224863_at
NM_002072 GNAQ Down 202756_s_at NM_002081.1 GPC1 Down 1555037_a_at
NM_005896.2 IDH1 Down 201193_at NM_005896.2 IDH1 Down 210046_s_at
NM_002168.2 IDH2 Down 219922_s_at NM_021070.2 LTBP3 Down 225540_at
NM_002374.3 MAP2 Down 209035_at NM_001012333.1 MDK Down 203238_s_at
NM_000435.1 NOTCH3 Down 212151_at NM_002585 PBX1 Down 204476_s_at
NM_000920 PC Down 206348_s_at NM_005391.1 PDK3 Down 203407_at
NM_002705.3 PPL Down 213093_at NM_002737.2 PRKCA Down 207011_s_at
NM_152880.2 PTK7 Down 203453_at NM_001038.4 SCNN1A Down 208998_at
NM_003355.2 UCP2 Down 208997_s_at NM_003355.2 UCP2 Down 205538_at
NM_003389 CORO2A Down 211712_s_at NM_003568.1 ANXA9 Down
209641_s_at NM_003786.2 ABCC3 Down 202481_at NM_004753.4 DHRS3 Down
223761_at NM_005117.2 FGF19 Down 225112_at NM_005759 ABI-2 Down
1554018_at NM_002510 GPNMB Down 203961_at NM_006393 NEBL Down
209791_at NM_007365 PADI2 Down 212325_at NM_014988.1 LIMCH1 Down
212328_at NM_014988.1 LIMCH1 Down 212327_at BX537916.1 LIMCH1 Down
227031_at NM_015132 SNX13 Down 202341_s_at NM_015271.2 TRIM2 Down
202342_s_at NM_015271.2 TRIM2 Down 205499_at NM_014467.1 SRPX2 Down
219188_s_at NM_014067.2 MACROD1 Down 221922_at NM_013296.3 GPSM2
Down 219014_at NM_016619.1 PLAC8 Down 220779_at NM_016233.1 PADI3
Down 225355_at AK096661.1 DKFZP761M1511 Down 222496_s_at
NM_019027.1 RBM47 Down 221748_s_at AF172820.1 MXRA6 Down
232914_s_at NM_032379.3 SYTL2 Down 218736_s_at NM_017734.2 PALMD
Down 226050_at NM_017905.3 TMCO3 Down 218729_at NM_020169.2 LXN
Down 220246_at NM_020397.2 CAMK1D Down 213839_at AB007969.1 CLMN
Down 232116_at NM_021180.2 GRHL3 Down 218858_at NM_022783.1 DEPDC6
Down 219263_at NM_024539.3 RNF128 Down 218342_s_at NM_024896.2
KIAA1815 Down 222603_at NM_024896.2 KIAA1815 Down 220584_at
NM_025094 FLJ22184 Down 224150_s_at NM_024491.2 CEP70 Down
238154_at NM_024491 BITE Down 228084_at NM_030821 PLA2G12 Down
212936_at NM_032042.3 C5orf21 Down 232176_at NM_032229.2 SLITRK6
Down 235976_at NM_032229.2 SLITRK6 Down 232481_s_at NM_032229.2
SLITRK6 Down 227134_at NM_032872.1 SYTL1 Down 228072_at NM_177963.2
SYT12 Down 226597_at NM_138393.1 C19orf32 Down 208158_s_at
NM_018030.3 OSBPL1A Down 209485_s_at NM_080597.2 OSBPL1A Down
226325_at NM_199165.1 ADSSL1 Down 225240_s_at NM_138962 MSI2 Down
234974_at NM_138801 LOC130589 Down 235256_s_at NM_138801.1 GALM
Down 226875_at NM_144658.2 DOCK11 Down 238029_s_at NM_152527.3
SLC16A14 Down 244261_at NM_170743.2 IL28RA Down 229616_s_at
NM_001012642.1 GRAMD2 Down 224990_at NM_174921.1 C4orf34 Down
227052_at NM_174921 LOC201895 Down 228067_at NM_207362.1 C2orf55
Down 224989_at AI824013 C4orf34 Down 235924_at N73742 Down
1565837_at AA215492 Down 227533_at AA732944 RALGPS2 Down 228959_at
AI676241 Down 232656_at AU145501 NAALADL2 Down 238441_at
NM_006252.3 PRKAA2 Down 238105_x_at AW294903 WNT7B
[0101] A substantial number of NF-kB signature genes regulate
immune cell chemotaxis (including T cell chemokines CCL2 and CCL5),
inflammation and immune regulation (Table 2).
TABLE-US-00003 TABLE 2 NF-.kappa.B signature genes involved in
inflammation and immune regulation Inflammation Adaptive Adhesion
Complement IL-6 HLA-B CD47 CFB IL-6 ST HLA-C ITGA2 C3 IL-8 ICAM1
ITGA5 IRAK2 TNFRSF9 ITGAM IRF1 LTB ITGAV CXCL1 CCL2 LAMC2 CXCL2
CCL5 CLDN1 CXCL3 TAP1 PLAUR TAP2 CCL20 TAPBP TLR2 CD83 TRAF1 IL23A
TRAF3 PSMB8 IFIH1 PSMB9 PSME2 IL32 NF-kB signature genes divided in
broad categories based on known functions. The category distinction
is not absolute since many genes are involved in multiple listed or
other functions. The "Inflammation" category refers to genes
involved in inflammation and innate immunity. "Adaptive" refers to
genes involved in the adaptive immune response. "Adhesion" refers
to genes involved in cell-cell or substrate interactions.
"Complement" includes genes involved in the complement pathway.
[0102] Using publically available microarray data from 126
different human lung cancer cell lines, cell-lines with high or low
NF-kB signature activity were next identified. The microarray data
was obtained from GEO at NCBI and Array Express at EBI. It consists
of 408 Affymetrix CEL files of 126 different lung cell lines. The
scores were calculated based on this set of samples so the high and
low are in reference to other cell lines within this group. The
probesets that were used to classify the samples were the 240
probes originally identified in the 5 cell lines (i.e. NF-kB
signature). The classifier was built and implemented as described
(classifier H in supplemental materials and methods) (Shedden et
al. 2008. Nat Med 14:822-827). The decision thresholds were made
based on the array of measures in the 408 lung CEL file data. The
weighted voting classification of each sample scores each gene
(probeset) based on where the signal value falls among all the
samples in the group. If the probeset was positively correlated
with the NF-KB signature (in the original 5 cell lines) then values
in the lower third receive a score of -1, values in the middle
third receive a 0, and values in the upper third receive a value of
1. If a gene was negatively correlated the values were reversed.
The scores for all probesets were summed to provide the final
classification score. The data sets used are listed below:
TABLE-US-00004 E-MTAB-37 Array Express GSE10021 GEO GSE10843 GEO
GSE13309 GEO GSE14315 GEO GSE14883 GEO GSE15240 GEO GSE16194 GEO
GSE17347 GEO GSE18454 GEO GSE21612 GEO GSE4824 GEO GSE5816 GEO
GSE6013 GEO GSE7562 GEO GSE8332 GEO
NF-kB signature scores were determined by building a classifier
that allowed determination of relative signature activity in the
different cell-lines, to classify cell lines with low or high NF-kB
signature. The classifier was built using methods previously
described (Shedden et al. 2008. Nat Med 14:822-827), as follows: A
majority vote classifier was used based on the probesets identified
in the NF-kB signature. The algorithm consists of three components:
the majority vote classifier, the individual classifiers, and the
training procedure. The classifier is a majority vote classifier
based on the probesets identified that correlate with NF-kB
activity in cell lines. The algorithm consisted of three
components: the majority vote classifier, the individual
classifiers, and the training procedure.
[0103] Terminology
[0104] C=majority vote of individual classifiers (sum of vote by
all probesets in classifier)
[0105] c.sub.k=individual classifier k (a single probeset and
thresholds for that probeset)
[0106] x.sub.j=sample j (microarray dataset for an array from a
single tumor sample)
[0107] G=set of genes used for individual classifiers (set of all
probesets in classifier)
[0108] g.sub.jk=gene expression value for sample j and probeset
k
[0109] S=sign (+/-) indicating trend relative to NF-kB activity,
+=high expression when NF-kB active=Blue group, -=high expression
when NF-kB inactive=Rosy group.
[0110] Majority Vote Classifier
[0111] Cj=.SIGMA. (c.sub.kj) was the continuous predictor for this
classifier, which was categorized into three groups according to
the following rule:
Pred ( xj ) = { Blue C j > 0.15 G Rosy C j < - 0.15 G Grey
otherwise ##EQU00001##
[0112] The value of 0.15 was heuristically determined depending on
the specific prediction corresponding to the Blue or Rosy class. A
Pred value of |G| (or -1*|G|) indicated complete agreement with
Rosy or Blue group while small values (e.g., 0.15*|G|) indicated
uncertainty in voting. As the NF-kB signature may predict NF-kB
activity, response to chemotherapy, prognosis, infiltration of
lymphocytes, and other biological properties the training process
can be used to further tune the constant to maximize predictive
success.
[0113] Individual Classifier
[0114] For each gk element of G [0115] Establish 3 quantiles
defined by 2 threshold values, LO and HI:
[0115] ck = { - 1 * S k g jk < LO + 1 * S k g jk > HI 0
otherwise ##EQU00002##
[0116] This had the effect of voting -1 for individual genes
under-expressed in the samples and +1 for over-expressed genes. The
vote was reversed if the gene was negatively correlated to the two
groups.
[0117] Training Process
[0118] The training process required a gene set G (e.g., a gene set
identified elsewhere in this proposal) and a reference dataset for
each sample class (tumor group) and a biologically known property
that might be predicted based on NF-kB activity. The training
process began by identifying the LO and HI thresholds for each
element gk. All gene expression values were ranked in the reference
data set from lowest expression level to highest expression level.
The LO threshold was the 33rd percentile value for gene k within
all samples of a given tumor class. This value could also be set
using a reference dataset of the tumor class to be classified.
Likewise the HI threshold was the 66th percentile value for gene k
within all samples of a given tumor class or the reference dataset.
Once the thresholds were set, the individual classification was
applied to all genes (gk).
[0119] Next the individual classifiers were summed to generate the
Majority Vote score Cj. This represented a continuous variable that
represents the relative NF-kB activity in the assessed sample.
Classification actually involved defining groups based on the
separation of this continuous variable into discrete groups: blue
and rosy. The constant (0.15 in the present example) was adjusted
to maximally correlate the blue group or rosy group with the
biological property that is predicted by the Majority Vote
Classifier. This constant was set using the reference dataset for
each tumor group and biological process.
[0120] Next, it was determined whether differences in NF-kB
signature correlated with differences NF-kB nuclear presence, i.e.,
activity. Four NF-kB low and 4 NF-kB high signature cell lines were
compared in a side by side analysis using EMSA. Based on this
analysis, 4 cell lines that were identified with high NF-kB
signature (H226, H157, H1299, H650) and 4 cell lines with low NF-kB
signature (H322, H1395, H522, H1437) were used for additional
studies.
[0121] Genes present in the NF-kB signature may also be regulated
by non-NF-kB pathways. Thus, while H226, H157, H1299 and H650
cell-lines have high expression of NF-kB signature genes, it is
unclear whether this is indeed due to NF-kB activity. To test this,
expression of the two signature genes with high co-regulation
scores (BIRC3 and TNFAIP3) was determined in these 8 cell-lines.
Despite variation in expression in individual cell-lines, NF-kB
high cell-lines had significantly higher mean expression of both
BIRC3 and TNFAIP3 compared to NF-kB low cell-lines (FIG. 4B).
[0122] To determine NF-kB involvement, H226, H157 and H1299 cells
were transduced with MiG and IkB.alpha.SR retroviruses (H650 showed
little or no retrovirus infection). Importantly, expression of both
genes was significantly reduced by IkB.alpha.SR expression (FIG.
4C). Similar results were obtained for additional genes including
CCL2, CCL5, CXCL1-3 and IL8 (FIG. 4D). These results therefore
indicate that high expression of NF-kB signature genes in these
cell-lines is dependent on NF-kB activity.
[0123] The next experiments were performed to evaluate an
association between the NF-kB signature and NF-kB DNA binding
activity. Importantly, EMSA showed that the 4 lines with high NF-kB
signature indeed had high kB-site binding activity (FIG. 4E).
However, the NF-kB signature low H322 cell-line also showed high
NF-kB activity (FIG. 4E). Therefore, NF-kB DNA binding activity
alone does not provide an unambiguous indication of NF-kB
transcriptional activity.
[0124] To seek a better understanding of NF-kB function in human
lung cancer, the signature was next used for studies in human lung
adenocarcinoma. Specifically, the correlation between NF-kB
regulated chemokine gene expression and T cell presence was
determined, as well as the association of these genes with patient
survival.
Example 4
Association of T Cell Chemokines, but not Neutrophil Chemokines,
with T Cell Presence in Human Lung Cancer
[0125] The association of different chemokines present in the NF-kB
signature with T cell presence was determined. For these studies,
the Consortium for the Molecular Classification of Lung
Adenocarcinoma (CMCLA) survival prediction study (59) was used.
CMCLA is the largest and most comprehensive study in which
microarray-based gene expression data from tumors was used to
predict survival of 442 early stage lung adenocarcinoma patients
(Shedden et al. 2008. Nat Med 14:822-827). Tumor samples used were
from four different institutions including the Moffitt Cancer
Center (Shedden et al. 2008). Specifically, the experiments
determined whether neutrophil (CXCL1-3 and IL8) and T cell
chemokines (CCL2, CCL5 and CXCL10) were differentially associated
with T cell presence (although CXCL10 did not achieve the criteria
for inclusion in the NF-kB signature, it was included in this
analysis because it was identified as a target gene in LLC and in
some human cell-lines). To detect T cells, T cell receptor .alpha.
(TRAC) and .beta. chain (TRBC1) gene expression was used as a
marker for T cell presence. As expected, TRAC expression was highly
correlated with TRBC1 expression (FIG. 5A). Importantly, CCL2, CCL5
and CXCL10, but not neutrophil chemokine expression (CXCL1-3 and
IL8), was positively correlated with these T cell markers (FIG.
5A). These results therefore indicate a functional link between T
cell chemokine expression and T cell presence in tumors.
Interestingly, expression of neutrophil chemokine genes was highly
correlated with each other (FIG. 5A) suggesting that NF-kB
regulated inflammatory and immune response functions predominate in
different tumors. To help understand how genes associated with
different NF-kB functions may be differentially regulated, their
association with key NF-kB activators was determined.
Example 5
LT.beta. Expression is Differentially Associated with T Cell
Chemokines in Human Lung Cancer
[0126] Multiple NF-kB-activating cytokines may be present in the
lung tumor microenvironment (Mantovani, A., Allavena, P., Sica, A.,
and Balkwill, F. 2008. Cancer-related inflammation. Nature
454:436-444). To identify cytokines potentially involved in
differential expression of T cell versus neutrophil chemokines,
association with expression of TNF.alpha., IL-1.alpha., IL-1.beta.,
LT.alpha.(TNF.beta.) and LT.beta. was tested. These cytokines not
only induce NF-kB activation but can also be transcriptionally
regulated by NF-kB. However, only LT.beta. was identified as an
NF-kB target gene in lung cancer cells (Table 2) and interestingly,
only LT.beta. showed a greater correlation with expression of T
cell chemokines versus neutrophil chemokines (FIGS. 5B and 5E).
LT.beta. receptor (LT.beta.R) engagement by heterodimers of
LT.alpha. and LT.beta. (LT.alpha.1/.beta.2) activates NF-kB RelA
and RelB, typically in association with p52 (Bonizzi and Karin,
2004. Trends Immunol 25:280-288; Dejardin et al., 2002. Immunity
17:525-535; Gommerman and Browning, 2003. Nature reviews.
Immunology 3:642-655; Wolf et al., 2010. Oncogene 29:5006-5018).
Mirroring correlation in tumors, soluble LT.alpha.1/.beta.2 (LT)
induced expression of T cell chemokines but not neutrophil
chemokines in human lung cancer cells (FIG. 5C). In contrast,
TNF.alpha. strongly induced both neutrophil and T cell chemokines
(FIG. 5D). These results suggest that differential expression of
NF-kB regulated T cell chemokines versus neutrophil chemokines in
human lung cancer could be achieved through agents that selectively
induce these different chemokine subsets, such as the LT.beta.R
ligand.
Example 6
NF-kB Signature Genes are Associated with Distinct Overall Survival
of Lung Cancer Patients
[0127] The above results indicate that NF-kB regulated inflammatory
and immune response genes are differentially expressed in lung
cancer. The next experiments determined whether expression of these
genes was also associated with distinct overall survival (OS) of
patients. Using the CMCLA dataset, 5-year OS of patients exhibiting
high versus low expression of these genes was determined using a
median cutoff. A striking effect on survival was seen for NF-kB
target genes involved in neutrophil chemotaxis and inflammation
(IL8, CXCL1, CXCL3 and IL6), all of which were associated with
significantly poor OS (FIG. 6A-D).
[0128] Poor survival in human cancer is associated with increased
metastasis. Both IL-8 and CXCL1 have been linked to increased
metastasis through effects on infiltrating myeloid cells (Sparmann
and Bar-Sagi, 2004. Cancer Cell 6:447-458; Acharyya et al. 2012.
Cell 150:165-178), suggesting association with poor survival can be
through increased metastatic dissemination. On the other hand, high
expression of a subset of genes involved in T cell chemotaxis and T
cell responses, including CCL2, LT.beta., ICAM-1 and CD83, was
associated with significantly improved OS (FIG. 7A-D). LT.beta.
association with improved OS was especially pronounced, perhaps
because LT.beta. can enhance expression of multiple genes involved
in T cell responses. Importantly, high expression of TCR.alpha. and
.beta. chain genes was also associated with significantly improved
OS in lung cancer patients (FIG. 7E). Therefore, while both T cell
presence and expression of NF-kB signature genes associated with T
cell responses is associated with improved OS, the high expression
of inflammatory genes is associated with poor OS. Hence, distinct
functions of NF-kB in human lung cancer are associated with
potentially different survival outcomes.
Example 7
NF-kB Activity in Human Lung Cancer is Strongly Associated with T
Cell Presence
[0129] The above findings indicate differential expression of
distinct NF-kB target genes in tumors. By allowing investigation of
combined expression of signature genes, principal component
analysis (PCA) can simplify evaluation of pathway-dependent gene
expression activity and has been widely used to derive and validate
gene signatures in various cancer studies (Chen et al., 2011.
Journal of the National Cancer Institute 103:1859-1870; Chen et
al., 2010. Breast cancer research and treatment 119:335-346; Chen
et al., 2010. Breast cancer research and treatment 120:25-34). The
first principal component (1.sup.st PC), (principal component
analysis--PCA) was used, as it accounted for the largest
variability in the data, to represent the overall expression level
for NF-kB activation. That is,
NF-kB activation score=.SIGMA.w.sub.ix.sub.i,
a weighted average expression among the NF-kB activated genes,
where x.sub.i represents gene i expression level, w.sub.i is the
corresponding weight (loading coefficient) with
.SIGMA.w.sub.i.sup.2=1, and the w.sub.i values maximize the
variance of .SIGMA.w.sub.ix.sub.i.
[0130] This approach has been used to derive a malignancy pathway
gene signature in a breast cancer study (Chen et al., 2010. Breast
Cancer Res Treat 119:335-346). The Cox proportional hazards model
was used to analyze the continuous NF-kB activation score to
determine if it can predict survival.
[0131] Of the 240 NF-kB signature probe sets present in Affymetrix
U133 Plus 2.0 microarrays, 159 were present (Table 3) in the CMCLA
dataset which used Affymetrix 133A (Shedden et al., 2008).
TABLE-US-00005 TABLE 3 Probe ID Gene Symbol Probe ID Gene Symbol
209099_x_at JAG1 218644_at PLEK2 216268_s_at JAG1 213524_s_at G0S2
202076_at BIRC2 220054_at IL23A 210538_s_at BIRC3 209788_s_at ERAP1
202357_s_at NA 218834_s_at TMEM132A 217767_at C3 218627_at DRAM1
211075_s_at CD47 219901_at FGD6 213857_s_at CD47 220658_s_at ARNTL2
202902_s_at CTSS 222001_x_at LOC728855 221903_s_at CYLD 209267_s_at
SLC39A8 203725_at GADD45A 219209_at IFIH1 202269_x_at GBP1
219759_at ERAP2 204224_s_at GCH1 218543_s_at PARP12 204470_at CXCL1
219033_at PARP8 209774_x_at CXCL2 220987_s_at NA 207850_at CXCL3
54970_at ZMIZ2 208729_x_at HLA-B 204823_at NAV3 209140_x_at HLA-B
220975_s_at C1QTNF1 211911_x_at HLA-B 205673_s_at ASB9 202637_s_at
ICAM1 212989_at SGMS1 215485_s_at ICAMI 202660_at ITPR2 202638_s_at
ICAM1 202053_s_at ALDH3A2 205207_at IL6 202054_s_at ALDH3A2
204863_s_at IL6ST 209442_x_at ANK3 211000_s_at IL6ST 211922_s_at
CAT 212195_at IL6ST 204920_at CPS1 212196_at IL6ST 217564_s_at CPS1
202859_x_at IL8 202295_s_at CTSH 207536_s_at TNFRSF9 209782_s_at
DBP 211786_at TNFRSF9 215116_s_at DNM1 210511_s_at INHBA 37996_s_at
DMPK 202531_at IRF1 204540_at EEF1A2 205032_at ITGA2 36499_at
CELSR2 201389_at ITGA5 212339_at EPB41L1 205786_s_at ITGAM
209189_at FOS 202351_at ITGAV 202756_s_at GPC1 202267_at LAMC2
201193_at IDH1 207517_at LAMC2 210046_s_at IDH2 207339_s_at LTB
219922_s_at LTBP3 203936_s_at MMP9 209035_at MDK 209239_at NFKB1
203238_s_at NOTCH3 209636_at NFKB2 212151_at PBX1 207535_s_at NFKB2
204476_s_at PC 203927_at NFKBIE 206348_s_at PDK3 201981_at PAPPA
203407_at PPL 206034_at SERPINB8 213093_at PRKCA 214866_at PLAUR
207011_s_at PTK7 209040_s_at PSMB8 203453_at SCNN1A 204279_at PSMB9
208998_at UCP2 201762_s_at PSME2 208997_s_at UCP2 206157_at PTX3
205538_at CORO2A 205205_at RELB 211712_s_at ANXA9 213194_at ROBO1
209641_s_at ABCC3 216598_s_at CCL2 202481_at DHRS3 1405_i_at CCL5
203961_at NEBL 205476_at CCL20 209791_at PADI2 202307_s_at TAP1
212325_at LIMCH1 204769_s_at TAP2 212328_at LIMCH1 208829_at TAPBP
212327_at LIMCH1 204924_at TLR2 202341_s_at TRIM2 202643_s_at
TNFAIP3 202342_s_at TRIM2 202644_s_at TNFAIP3 205499_at SRPX2
205599_at TRAF1 219188_s_at MACROD1 208315_x_at TRAF3 221922_at
GPSM2 203299_s_at AP1S2 219014_at PLAC8 218182_s_at CLDN1 220779_at
PADI3 203828_s_at IL32 221748_s_at TNS1 204440_at CD83 218736_s_at
PALMD 221841_s_at KLF4 218729_at LXN 212636_at QKI 220246_at CAMK1D
204702_s_at NFE2L3 213839_at CLMN 204589_at NUAK1 218858_at DEPDC6
205100_at GFPT2 219263_at RNF128 203320_at SH2B3 218342_s_at ERMP1
201489_at PPIF 220584_at FLJ22184 202073_at OPTN 212936_at FAM172A
202074_s_at OPTN 208158_s_at OSBPL1A 207196_s_at TNIP1 209485_s_at
OSBPL1A 204341_at TRIM16 213667_at SRCAP 220091_at SLC2A6
[0132] NF-kB signature "driver" genes (i.e., the top 10 genes with
highest PCA weight) were identified as GBP1, PSMB9, IRF1, TAP1,
TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and NFKBIE; all are upregulated
by NF-kB (see Table 1). First principal component (PC1) is
associated with the largest variance of the data (e.g., variance of
NF-kB signature activity in the CMCLA dataset) followed by each
succeeding PC. Using association with expression of driver genes,
it was found that PC1 but not PC2 is associated with NF-kB activity
(FIG. 8A). Using the NF-kB signature PC1, it was determined whether
NF-kB activity was associated with T cell presence. For these
studies, PC1 of T cell receptor .alpha. and .beta. chain gene
expression (FIG. 8B, top panel) was used. Importantly, a strong
association was seen between NF-kB activity and T cell presence
(r=0.68) (FIG. 8C). Therefore, while NF-kB target genes are
associated with different functions in tumors, the overall NF-kB
activity as determined by PCA is strongly associated with T cell
presence.
[0133] In addition to inflammation, NF-kB also exerts pro-tumor
effects through cancer cell-intrinsic regulation of cell survival
and proliferation (Barbie et al. 2009. Nature 462:108-112; Meylan
et al., 2009. Nature 462:104-107; Maeda et al., 2005. Cell
121:977-990; Karin and Greten, 2005. Nat Rev Immunol 5:749-759;
Greten et al., 2004. Cell 118:285-296; Takahashi et al., 2010.
Cancer Cell 17:89-97; Basseres et al., 2010. Cancer Res
70:3537-3546). Previous studies described a breast and NSCLC
malignancy-risk (MR) signature that is rich in proliferation and
cell cycle genes (Chen et al., 2011. Journal of the National Cancer
Institute 103:1859-1870; Chen et al., 2010. Breast cancer research
and treatment 119:335-346; Chen et al., 2010. Breast cancer
research and treatment 120:25-34). Using the MR signature PC1, it
was next determined whether NF-kB activity was also associated with
cancer cell proliferation. However, little correlation between the
NF-kB and MR signatures was noticed (r=0.21) (FIG. 8D). Thus, NF-kB
activity is associated with T cell presence and potential immune
surveillance functions but not with cancer cell proliferation.
[0134] Whether expression of the 10 driver genes mentioned above
(GBP1, PSMB9, IRF1, TAP1, TNFAIP3, CCL5, PSMB8, IL32, SH2B3 and
NFKBIE) was associated with overall NF-kB activity and T cell
presence was also determined Indeed, PC1 of these genes (FIG. 8B,
bottom panel) was highly correlated with the NF-kB signature PC1
(r=0.92) (FIG. 8E). Therefore, this smaller 10-gene signature can
be used in lieu of the NF-kB signature to determine NF-kB activity.
Importantly, the 10-gene PC1 was also strongly correlated with the
T cell PC1 (r=0.79) (FIG. 8F) but not with the MR signature PC1
(r=0.19) (FIG. 8G). Using an additional lung cancer microarray
dataset (n=133) (Zhu et al., 2010. Journal of Clinical Oncology
28:4417-4424), there was a similarly high correlation of the
10-gene signature with T cell presence (r=0.8) but not with the MR
signature (r=-0.16) (FIG. 8H).
[0135] In conclusion, the present findings from both mouse and
human studies indicate that tumor NF-kB activity is strongly
associated with T cell presence and immune surveillance in lung
cancer.
Example 8
NF-kB Signature Classifies Multiple Tumor Types into High and Low
for the NF-kB Signature, and Correlates with Presence of T
Cells
[0136] A majority vote classifier as mentioned above can be used to
classify different tumor types (including colon adenocarcinoma,
renal carcinoma, ovarian cancer, kidney tumor, prostate tumors, and
melanoma) into high and low for NF-kB signature, with similar
results expected. High NF-kB signature corresponds to positive
numbers and low activity corresponds to negative numbers. Each
tumor class had individual samples with high, intermediate, and low
NF-kB signatures. Exemplary data is shown in FIG. 9, in which PCA,
performed as described above, was used to demonstrate a high degree
of correlation between T cell presence and the 159 genes listed in
Table 3 (r=0.84) or the 10-gene signature (r=0.95) in melanoma.
This demonstrated that the classification procedure can be used in
many tumor types, and also that tumors exhibit a continuum of
values representing their NF-kB activity.
Other Embodiments
[0137] It is to be understood that while the invention has been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the invention, which is defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
* * * * *